+0001: from itertools import compress

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+0002: from scipy import sparse

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+0003: from scipy.integrate import solve_ivp

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+0004: from scipy.optimize import minimize, approx_fprime

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+0005: from scipy.stats import lognorm

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+0006: import numpy as np

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+0007: from scipy.interpolate import make_interp_spline

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+0008: from scipy.misc import derivative

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 0009: cimport numpy as np

 0010: cimport cython

+0011: import time, sympy

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+0012: from sympy import MutableDenseNDimArray as Array

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 0013: 

+0014: try:

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/* … */
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    __pyx_L7_try_end:;
  }

 0015:     # Optional support for nested sampling.

+0016:     import nestle

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+0017: except ImportError:

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 0019: 

 0020: 

+0021: import pyross.deterministic

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 0022: cimport pyross.deterministic

+0023: import pyross.contactMatrix

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+0024: from pyross.utils_python import minimization, nested_sampling

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 0025: from libc.math cimport sqrt, log, INFINITY

+0026: cdef double PI = 3.14159265359

  __pyx_v_6pyross_9inference_PI = 3.14159265359;

 0027: 

 0028: 

+0029: DTYPE   = np.float

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+0030: ctypedef np.float_t DTYPE_t

typedef __pyx_t_5numpy_float_t __pyx_t_6pyross_9inference_DTYPE_t;

 0031: ctypedef np.uint8_t BOOL_t

 0032: 

 0033: @cython.wraparound(False)

 0034: @cython.boundscheck(False)

 0035: @cython.cdivision(True)

 0036: @cython.nonecheck(False)

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  PyArrayObject *J_mat;
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  PyArrayObject *flat_indices1;
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 0111:         '''Compute the maximum a-posteriori (MAP) estimate of the parameters of the SIR type model.

 0112:         This function assumes that full data on all classes is available (with latent variables, use SIR_type.latent_inference).

 0113: 

 0114:         IN DEVELOPMENT: Parameters that support age-dependent values can be inferred age-dependently by setting the guess

 0115:         to a numpy.array of self.M initial values. By default, each age-dependent parameter is inferred independently.

 0116:         If the relation of the different parameters is known, a scale factor of the initial guess can be inferred instead

 0117:         by setting infer_scale_parameter to True for each age-dependent parameter where this is wanted. Note that

 0118:         computing hessians for age-dependent rates is not yet supported. This functionality might be changed in the

 0119:         future without warning.

 0120: 

 0121:         Parameters

 0122:         ----------

 0123:         keys: list

 0124:             A list of names for parameters to be inferred

 0125:         guess: numpy.array or list

 0126:             Prior expectation (and initial guess) for the parameter values. Age-dependent

 0127:             rates can be inferred by supplying a guess that is an array instead a single float.

 0128:         stds: numpy.array

 0129:             Standard deviations for the log normal prior of the parameters

 0130:         bounds: 2d numpy.array

 0131:             Bounds for the parameters (number of parameters x 2)

 0132:         x: 2d numpy.array

 0133:             Observed trajectory (number of data points x (age groups * model classes))

 0134:         Tf: float

 0135:             Total time of the trajectory

 0136:         Nf: float

 0137:             Number of data points along the trajectory

 0138:         contactMatrix: callable

 0139:             A function that returns the contact matrix at time t (input).

 0140:         tangent: bool, optional

 0141:             Set to True to do inference in tangent space (might be less robust but a lot faster). Default is False.

 0142:         infer_scale_parameter: bool or list of bools (size: number of age-dependenly specified parameters)

 0143:             Decide if age-dependent parameters are supposed to be inferred separately (default) or if a scale parameter

 0144:             for the guess should be inferred. This can be set either globally for all age-dependent parameters or for each

 0145:             age-dependent parameter individually

 0146:         verbose: bool, optional

 0147:             Set to True to see intermediate outputs from the optimizer.

 0148:         ftol: float

 0149:             Relative tolerance of logp

 0150:         eps: float

 0151:             Disallow parameters closer than `eps` to the boundary (to avoid numerical instabilities).

 0152:         global_max_iter: int, optional

 0153:             Number of global optimisations performed.

 0154:         local_max_iter: int, optional

 0155:             Number of local optimisation performed.

 0156:         global_atol: float

 0157:             The absolute tolerance for global optimisation.

 0158:         enable_global: bool, optional

 0159:             Set to True to enable global optimisation.

 0160:         enable_local: bool, optional

 0161:             Set to True to enable local optimisation.

 0162:         cma_processes: int, optional

 0163:             Number of parallel processes used for global optimisation.

 0164:         cma_population: int, optional

 0165:             The number of samples used in each step of the CMA algorithm.

 0166:         cma_stds: int, optional

 0167:             The standard deviation used in cma global optimisation. If not specified, `cma_stds` is set to `stds`.

 0168: 

 0169:         Returns

 0170:         -------

 0171:         estimates: numpy.array

 0172:             The MAP parameter estimate

 0173:         '''

 0174:         # Transfer the guesses, stds, ... which can contain arrays as entries for age-dependend rates to a flat vector for inference.

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 0221:         '''Compute the log-evidence and weighted samples of the a-posteriori distribution of the parameters of a SIR type model

 0222:         using nested sampling as implemented in the `nestle` Python package. This function assumes that full data on

 0223:         all classes is available.

 0224: 

 0225:         This function provides a computational alterantive to `log_G_evidence` and `infer_parameters`. It does not use

 0226:         the Laplace approximation to compute the evidence and, in addition,  returns a set of representative samples that can

 0227:         be used to compute a posterior mean estimate (insted of the MAP estimate). This approach approach is much more resource

 0228:         intensive and typically only viable for small models or tangent space inference.

 0229: 

 0230:         Parameters

 0231:         ----------

 0232:         keys: list

 0233:             A list of names for parameters to be inferred

 0234:         guess: numpy.array or list

 0235:             Prior expectation (and initial guess) for the parameter values. Age-dependent

 0236:             rates can be inferred by supplying a guess that is an array instead a single float.

 0237:         stds: numpy.array

 0238:             Standard deviations for the log normal prior of the parameters

 0239:         x: 2d numpy.array

 0240:             Observed trajectory (number of data points x (age groups * model classes))

 0241:         Tf: float

 0242:             Total time of the trajectory

 0243:         Nf: float

 0244:             Number of data points along the trajectory

 0245:         contactMatrix: callable

 0246:             A function that returns the contact matrix at time t (input).

 0247:         bounds: np.array(len(guess), 2), optional

 0248:             Bound the prior within the values specified by this. This can be used to avoid sampling the posterior

 0249:             in regions where the solution is numerically unstable (e.g. parameters close to 0). Any bound introduces

 0250:             a bias to the result, therefore one must make sure that the blocked regions are negligible.

 0251:         tangent: bool, optional

 0252:             Set to True to do inference in tangent space (might be less robust but a lot faster). Default is False.

 0253:         infer_scale_parameter: bool or list of bools (size: number of age-dependenly specified parameters)

 0254:             Decide if age-dependent parameters are supposed to be inferred separately (default) or if a scale parameter

 0255:             for the guess should be inferred. This can be set either globally for all age-dependent parameters or for each

 0256:             age-dependent parameter individually

 0257:         verbose: bool, optional

 0258:             Set to True to see intermediate outputs from the nested sampling procedure.

 0259:         queue_size: int

 0260:             Size of the internal queue of samples of the nested sampling algorithm. The log-likelihood of these samples

 0261:             is computed in parallel (if queue_size > 1).

 0262:         max_workers: int

 0263:             The maximal number of processes used to compute samples.

 0264:         npoints: int

 0265:             Argument of `nestle.sample`. The number of active points used in the nested sampling algorithm. The higher the

 0266:             number the more accurate and expensive is the evidence computation.

 0267:         method: str

 0268:             Nested sampling method used int `nestle.sample`, see their documentation. Default is `single`, for multimodel posteriors,

 0269:             use `multi`.

 0270:         max_iter: int

 0271:             Maximum number of iterations of the nested sampling algorithm.

 0272:         dlogz: float, optional

 0273:             Stopping threshold for the estimated error of the log-evidence. This option is mutually exclusive with `decline_factor`.

 0274:         decline_factor: float, optional

 0275:             Stop the iteration when the weight (likelihood times prior volume) of newly saved samples has been declining for

 0276:             `decline_factor * nsamples` consecutive samples. This option is mutually exclusive with `dlogz`.

 0277: 

 0278:         Returns

 0279:         -------

 0280:         log_evidence:

 0281:             The nested sampling estimate of the log-evidence.

 0282:         (samples, weights):

 0283:             A set of weighted samples approximating the posterios distribution.

 0284:         '''

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 0352:         """

 0353:         Compute the maximum a-posteriori (MAP) estimate of the change of control parameters for a SIR type model in

 0354:         lockdown. The lockdown is modelled by scaling the contact matrices for contact at work, school, and other

 0355:         (but not home) uniformly in all age groups. This function infers the scaling parameters assuming that full data

 0356:         on all classes is available (with latent variables, use SIR_type.latent_infer_control).

 0357: 

 0358:         Parameters

 0359:         ----------

 0360:         guess: numpy.array

 0361:             Prior expectation (and initial guess) for the control parameter values

 0362:         stds: numpy.array

 0363:             Standard deviations for the log normal prior of the control parameters

 0364:         x: 2d numpy.array

 0365:             Observed trajectory (number of data points x (age groups * model classes))

 0366:         Tf: float

 0367:             Total time of the trajectory

 0368:         Nf: float

 0369:             Number of data points along the trajectory

 0370:         generator: pyross.contactMatrix

 0371:             A pyross.contactMatrix object that generates a contact matrix function with specified lockdown

 0372:             parameters.

 0373:         bounds: 2d numpy.array

 0374:             Bounds for the parameters (number of parameters x 2).

 0375:             Note that the upper bound must be smaller than the absolute physical upper bound minus epsilon

 0376:         intervention_fun: callable, optional

 0377:             The calling signature is `intervention_func(t, **kwargs)`, where t is time and kwargs are other keyword arguments for the function.

 0378:             The function must return (aW, aS, aO). If not set, assume intervention that's constant in time and infer (aW, aS, aO).

 0379:         tangent: bool, optional

 0380:             Set to True to use tangent space inference. Default is false.

 0381:         verbose: bool, optional

 0382:             Set to True to see intermediate outputs from the optimizer.

 0383:         ftol: double

 0384:             Relative tolerance of logp

 0385:         eps: double

 0386:             Disallow paramters closer than `eps` to the boundary (to avoid numerical instabilities).

 0387:         global_max_iter: int, optional

 0388:             Number of global optimisations performed.

 0389:         local_max_iter: int, optional

 0390:             Number of local optimisation performed.

 0391:         global_atol: float

 0392:             The absolute tolerance for global optimisation.

 0393:         enable_global: bool, optional

 0394:             Set to True to enable global optimisation.

 0395:         enable_local: bool, optional

 0396:             Set to True to enable local optimisation.

 0397:         cma_processes: int, optional

 0398:             Number of parallel processes used for global optimisation.

 0399:         cma_population: int, optional

 0400:             The number of samples used in each step of the CMA algorithm.

 0401:         cma_stds: int, optional

 0402:             The standard deviation used in cma global optimisation. If not specified, `cma_stds` is set to `stds`.

 0403: 

 0404:         Returns

 0405:         -------

 0406:         res: numpy.array

 0407:             MAP estimate of the control parameters

 0408:         """

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 0434:             MAP estimates

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 0476:         '''

 0477:         Computes the Fisher Information Matrix (FIM) of the stochastic model.

 0478:         Parameters

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 0480:         param_keys: list

 0481:             A list of parameters to be inferred.

 0482:         init_fltr: boolean array

 0483:             True for initial conditions to be inferred.

 0484:             Shape = (nClass*M)

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 0487:             MAP parameter and initial condition estimate (computed for example with SIR_type.latent_inference).

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 0505:         infer_scale_parameter: bool or list of bools (size: number of age-dependenly specified parameters)

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 0507:             for the guess should be inferred. This can be set either globally for all age-dependent parameters or for each

 0508:             age-dependent parameter individually

 0509:         Returns

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 0511:         FIM: 2d numpy.array

 0512:             The Fisher Information Matrix

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 0614:         '''

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static char __pyx_doc_6pyross_9inference_8SIR_type_32latent_infer_parameters[] = "\n        Compute the maximum a-posteriori (MAP) estimate of the parameters and the initial conditions of a SIR type model\n        when the classes are only partially observed. Unobserved classes are treated as latent variables.\n\n        Parameters\n        ----------\n        param_keys: list\n            A list of parameters to be inferred.\n        init_fltr: boolean array\n            True for initial conditions to be inferred.\n            Shape = (nClass*M)\n            Total number of True = total no. of variables - total no. of observed\n        guess: numpy.array or list\n            Prior expectation for the parameter values listed, and prior for initial conditions.\n            Expect of length len(param_keys)+ (total no. of variables - total no. of observed).\n            Age-dependent rates can be inferred by supplying a guess that is an array instead a single float.\n        stds: numpy.array\n            Standard deviations for the log normal prior.\n        obs: 2d numpy.array\n            The observed trajectories with reduced number of variables\n            (number of data points, (age groups * observed model classes))\n        fltr: 2d numpy.array\n            A matrix of shape (no. observed variables, no. total variables),\n            such that obs_{ti} = fltr_{ij} * X_{tj}\n        Tf: float\n            Total time of the trajectory\n        Nf: int\n            Total number of data points along the trajectory\n        contactMatrix: callable\n            A function that returns the contact matrix at time t (input).\n        bounds: 2d numpy.array\n            Bounds for the parameters + initial conditions\n            ((number of parameters + number of initial conditions) x 2).\n            Better bounds makes it easier to find the true global minimum.\n        tangent: bool, optional\n            Set to True to do inference in tangent space (might be less robust but a lot faster). Default is False.\n        infer_scale_parameter: bool or ""list of bools (size: number of age-dependenly specified parameters)\n            Decide if age-dependent parameters are supposed to be inferred separately (default) or if a scale parameter\n            for the guess should be inferred. This can be set either globally for all age-dependent parameters or for each\n            age-dependent parameter individually\n        verbose: bool, optional\n            Set to True to see intermediate outputs from the optimizer.\n        ftol: float, optional\n            Relative tolerance\n        global_max_iter: int, optional\n            Number of global optimisations performed.\n        local_max_iter: int, optional\n            Number of local optimisation performed.\n        global_atol: float\n            The absolute tolerance for global optimisation.\n        enable_global: bool, optional\n            Set to True to enable global optimisation.\n        enable_local: bool, optional\n            Set to True to enable local optimisation.\n        cma_processes: int, optional\n            Number of parallel processes used for global optimisation.\n        cma_population: int, optional\n            The number of samples used in each step of the CMA algorithm.\n        cma_stds: int, optional\n            The standard deviation used in cma global optimisation. If not specified, `cma_stds` is set to `stds`.\n        obs0: numpy.array, optional\n            Observed initial condition, if more detailed than obs[0]\n        fltr0: 2d numpy.array, optional\n            Matrix filter for obs0\n\n        Returns\n        -------\n        params: nested list\n            MAP estimate of paramters (nested if some parameters are age dependent) and initial values of the classes.\n        ";
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 0795:         """

 0796:         Compute the maximum a-posteriori (MAP) estimate of the parameters and the initial conditions of a SIR type model

 0797:         when the classes are only partially observed. Unobserved classes are treated as latent variables.

 0798: 

 0799:         Parameters

 0800:         ----------

 0801:         param_keys: list

 0802:             A list of parameters to be inferred.

 0803:         init_fltr: boolean array

 0804:             True for initial conditions to be inferred.

 0805:             Shape = (nClass*M)

 0806:             Total number of True = total no. of variables - total no. of observed

 0807:         guess: numpy.array or list

 0808:             Prior expectation for the parameter values listed, and prior for initial conditions.

 0809:             Expect of length len(param_keys)+ (total no. of variables - total no. of observed).

 0810:             Age-dependent rates can be inferred by supplying a guess that is an array instead a single float.

 0811:         stds: numpy.array

 0812:             Standard deviations for the log normal prior.

 0813:         obs: 2d numpy.array

 0814:             The observed trajectories with reduced number of variables

 0815:             (number of data points, (age groups * observed model classes))

 0816:         fltr: 2d numpy.array

 0817:             A matrix of shape (no. observed variables, no. total variables),

 0818:             such that obs_{ti} = fltr_{ij} * X_{tj}

 0819:         Tf: float

 0820:             Total time of the trajectory

 0821:         Nf: int

 0822:             Total number of data points along the trajectory

 0823:         contactMatrix: callable

 0824:             A function that returns the contact matrix at time t (input).

 0825:         bounds: 2d numpy.array

 0826:             Bounds for the parameters + initial conditions

 0827:             ((number of parameters + number of initial conditions) x 2).

 0828:             Better bounds makes it easier to find the true global minimum.

 0829:         tangent: bool, optional

 0830:             Set to True to do inference in tangent space (might be less robust but a lot faster). Default is False.

 0831:         infer_scale_parameter: bool or list of bools (size: number of age-dependenly specified parameters)

 0832:             Decide if age-dependent parameters are supposed to be inferred separately (default) or if a scale parameter

 0833:             for the guess should be inferred. This can be set either globally for all age-dependent parameters or for each

 0834:             age-dependent parameter individually

 0835:         verbose: bool, optional

 0836:             Set to True to see intermediate outputs from the optimizer.

 0837:         ftol: float, optional

 0838:             Relative tolerance

 0839:         global_max_iter: int, optional

 0840:             Number of global optimisations performed.

 0841:         local_max_iter: int, optional

 0842:             Number of local optimisation performed.

 0843:         global_atol: float

 0844:             The absolute tolerance for global optimisation.

 0845:         enable_global: bool, optional

 0846:             Set to True to enable global optimisation.

 0847:         enable_local: bool, optional

 0848:             Set to True to enable local optimisation.

 0849:         cma_processes: int, optional

 0850:             Number of parallel processes used for global optimisation.

 0851:         cma_population: int, optional

 0852:             The number of samples used in each step of the CMA algorithm.

 0853:         cma_stds: int, optional

 0854:             The standard deviation used in cma global optimisation. If not specified, `cma_stds` is set to `stds`.

 0855:         obs0: numpy.array, optional

 0856:             Observed initial condition, if more detailed than obs[0]

 0857:         fltr0: 2d numpy.array, optional

 0858:             Matrix filter for obs0

 0859: 

 0860:         Returns

 0861:         -------

 0862:         params: nested list

 0863:             MAP estimate of paramters (nested if some parameters are age dependent) and initial values of the classes.

 0864:         """

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 1092:         '''Compute the log-evidence and weighted samples of the a-posteriori distribution of the parameters of a SIR type model

 1093:         with latent variables using nested sampling as implemented in the `nestle` Python package.

 1094: 

 1095:         This function provides a computational alterantive to `latent_infer_parameters`. It computes an estimate of the evidence and,

 1096:         in addition, returns a set of representative samples that can be used to compute a posterior mean estimate (insted of the MAP

 1097:         estimate). This approach approach is much more resource intensive and typically only viable for small models or tangent space inference.

 1098: 

 1099:         Parameters

 1100:         ----------

 1101:         param_keys: list

 1102:             A list of parameters to be inferred.

 1103:         init_fltr: boolean array

 1104:             True for initial conditions to be inferred.

 1105:             Shape = (nClass*M)

 1106:             Total number of True = total no. of variables - total no. of observed

 1107:         guess: numpy.array or list

 1108:             Prior expectation for the parameter values listed, and prior for initial conditions.

 1109:             Expect of length len(param_keys)+ (total no. of variables - total no. of observed).

 1110:             Age-dependent rates can be inferred by supplying a guess that is an array instead a single float.

 1111:         stds: numpy.array

 1112:             Standard deviations for the log normal prior.

 1113:         obs: 2d numpy.array

 1114:             The observed trajectories with reduced number of variables

 1115:             (number of data points, (age groups * observed model classes))

 1116:         fltr: 2d numpy.array

 1117:             A matrix of shape (no. observed variables, no. total variables),

 1118:             such that obs_{ti} = fltr_{ij} * X_{tj}

 1119:         Tf: float

 1120:             Total time of the trajectory

 1121:         Nf: int

 1122:             Total number of data points along the trajectory

 1123:         contactMatrix: callable

 1124:             A function that returns the contact matrix at time t (input).

 1125:         bounds: np.array(len(guess), 2), optional

 1126:             Bound the prior within the values specified by this array. This can be used to avoid sampling the posterior

 1127:             in regions where the solution is numerically unstable (e.g. parameters close to 0). Any bound introduces

 1128:             a bias to the result, therefore one must make sure that the blocked regions are negligible.

 1129:         obs0: numpy.array, optional

 1130:             Observed initial condition, if more detailed than obs[0]

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 1132:             Matrix filter for obs0

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 1137:             for the guess should be inferred. This can be set either globally for all age-dependent parameters or for each

 1138:             age-dependent parameter individually

 1139:         verbose: bool, optional

 1140:             Set to True to see intermediate outputs from the nested sampling procedure.

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 1142:             Size of the internal queue of samples of the nested sampling algorithm. The log-likelihood of these samples

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 1145:             The maximal number of processes used to compute samples.

 1146:         npoints: int

 1147:             Argument of `nestle.sample`. The number of active points used in the nested sampling algorithm. The higher the

 1148:             number the more accurate and expensive is the evidence computation.

 1149:         method: str

 1150:             Nested sampling method used int `nestle.sample`, see their documentation. Default is `single`, for multimodel posteriors,

 1151:             use `multi`.

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 1153:             Maximum number of iterations of the nested sampling algorithm.

 1154:         dlogz: float, optional

 1155:             Stopping threshold for the estimated error of the log-evidence. This option is mutually exclusive with `decline_factor`.

 1156:         decline_factor: float, optional

 1157:             Stop the iteration when the weight (likelihood times prior volume) of newly saved samples has been declining for

 1158:             `decline_factor * nsamples` consecutive samples. This option is mutually exclusive with `dlogz`.

 1159: 

 1160:         Returns

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 1163:             The nested sampling estimate of the log-evidence.

 1164:         (samples, weights):

 1165:             A set of weighted samples approximating the posterios distribution.

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 1259:         """

 1260:         Compute the maximum a-posteriori (MAP) estimate of the change of control parameters for a SIR type model in

 1261:         lockdown with partially observed classes. The unobserved classes are treated as latent variables. The lockdown

 1262:         is modelled by scaling the contact matrices for contact at work, school, and other (but not home) uniformly in

 1263:         all age groups. This function infers the scaling parameters.

 1264: 

 1265:         Parameters

 1266:         ----------

 1267:         keys: list

 1268:             A list of keys for the control parameters to be inferred.

 1269:         guess: numpy.array

 1270:             Prior expectation (and initial guess) for the control parameter values.

 1271:         stds: numpy.array

 1272:             Standard deviations for the log normal prior of the control parameters

 1273:         x0: numpy.array

 1274:             Initial conditions.

 1275:         obs:

 1276:             Observed trajectory (number of data points x (age groups * observed model classes)).

 1277:         fltr: boolean sequence or array

 1278:             True for observed and False for unobserved classes.

 1279:             e.g. if only Is is known for SIR with one age group, fltr = [False, False, True]

 1280:         Tf: float

 1281:             Total time of the trajectory

 1282:         Nf: float

 1283:             Number of data points along the trajectory

 1284:         generator: pyross.contactMatrix

 1285:             A pyross.contactMatrix object that generates a contact matrix function with specified lockdown

 1286:             parameters.

 1287:         bounds: 2d numpy.array

 1288:             Bounds for the parameters (number of parameters x 2).

 1289:             Note that the upper bound must be smaller than the absolute physical upper bound minus epsilon

 1290:         intervention_fun: callable, optional

 1291:             The calling signature is `intervention_func(t, **kwargs)`, where t is time and kwargs are other keyword arguments for the function.

 1292:             The function must return (aW, aS, aO). If not set, assume intervention that's constant in time and infer (aW, aS, aO).

 1293:         tangent: bool, optional

 1294:             Set to True to use tangent space inference. Default is false.

 1295:         verbose: bool, optional

 1296:             Set to True to see intermediate outputs from the optimizer.

 1297:         ftol: double

 1298:             Relative tolerance of logp

 1299:         global_max_iter: int, optional

 1300:             Number of global optimisations performed.

 1301:         local_max_iter: int, optional

 1302:             Number of local optimisation performed.

 1303:         global_atol: float

 1304:             The absolute tolerance for global minimisation.

 1305:         enable_global: bool, optional

 1306:             Set to True to enable global optimisation.

 1307:         enable_local: bool, optional

 1308:             Set to True to enable local optimisation.

 1309:         cma_processes: int, optional

 1310:             Number of parallel processes used for global optimisation.

 1311:         cma_population: int, optional

 1312:             The number of samples used in each step of the CMA algorithm.

 1313:         cma_stds: int, optional

 1314:             The standard deviation used in cma global optimisation. If not specified, `cma_stds` is set to `stds`.

 1315:         full_output: bool, optional

 1316:             Set to True to return full minimization output

 1317: 

 1318:         Returns

 1319:         -------

 1320:         params: numpy.array

 1321:             MAP estimate of control parameters

 1322:         y_result: float (returned if full_output is True)

 1323:             logp for MAP estimates

 1324: 

 1325:         """

+1326:         fltr = pyross.utils.process_fltr(fltr, Nf)

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 1560:         '''Returns the full initial condition given partial initial conditions and the observed data

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 1562:         Parameters

 1563:         ----------

 1564:         partial_inits: 1d np.array

 1565:             Partial initial conditions.

 1566:         obs_inits: 1d np.array

 1567:             The observed initial conditions.

 1568:         init_fltr: 1d np.array

 1569:             A vector boolean fltr that yields the partial initis given full initial conditions.

 1570:         fltr: 2d np.array

 1571:             A matrix fltr that yields the observed data from full data. Same as the one used for latent_infer_parameters.

 1572: 

 1573:         Returns

 1574:         -------

 1575:         x0: 1d np.array

 1576:             The full initial condition.

 1577:         '''

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 1921:         """An light weight integrate method similar to `simulate` in pyross.deterministic

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 1923:         Parameters

 1924:         ----------

 1925:         x0: np.array

 1926:             Initial state of the given model

 1927:         t1: float

 1928:             Initial time of integrator

 1929:         t2: float

 1930:             Final time of integrator

 1931:         steps: int

 1932:             Number of time steps for numerical integrator evaluation.

 1933:         model: pyross model

 1934:             Model to integrate (pyross.deterministic.SIR etc)

 1935:         contactMatrix: python function(t)

 1936:              The social contact matrix C_{ij} denotes the

 1937:              average number of contacts made per day by an

 1938:              individual in class i with an individual in class j

 1939:         maxNumSteps:

 1940:             The maximum number of steps taken by the integrator.

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 1942:         Returns

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 1944:         sol: np.array

 1945:             The state of the system evaulated at the time point specified. Only used if det_method is set to 'solve_ivp'.

 1946:         """

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 2032: 

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 2035: @cython.boundscheck(False)

 2036: @cython.cdivision(True)

 2037: @cython.nonecheck(False)

+2038: cdef class SIR(SIR_type):

struct __pyx_obj_6pyross_9inference_SIR {
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 2039:     """

 2040:     Susceptible, Infected, Removed (SIR)

 2041: 

 2042:     * Ia: asymptomatic

 2043:     * Is: symptomatic

 2044: 

 2045:     To initialise the SIR class,

 2046: 

 2047:     Parameters

 2048:     ----------

 2049:     parameters: dict

 2050:         Contains the following keys:

 2051: 

 2052:         alpha: float

 2053:             Ratio of asymptomatic carriers

 2054:         beta: float

 2055:             Infection rate upon contact

 2056:         gIa: float

 2057:             Recovery rate for asymptomatic

 2058:         gIs: float

 2059:             Recovery rate for symptomatic

 2060:         fsa: float

 2061:             The fraction of symptomatic people who are self-isolating

 2062:     M: int

 2063:         Number of age groups

 2064:     fi: float numpy.array

 2065:         Fraction of each age group

 2066:     N: int

 2067:         Total population

 2068:     steps: int

 2069:         The number of internal integration steps performed between the observed points (not used in tangent space inference).

 2070:         The minimal is 4, as required by the cubic spline fit used for interpolation.

 2071:         For robustness, set steps to be large, det_method='LSODA', lyapunov_method='LSODA'.

 2072:         For speed, set steps to be 4, det_method='RK2', lyapunov_method='euler'.

 2073:         For a combination of the two, choose something in between.

 2074:     det_method: str, optional

 2075:         The integration method used for deterministic integration.

 2076:         Choose one of 'LSODA', 'RK45', 'RK2' and 'euler'. Default is 'LSODA'.

 2077:     lyapunov_method: str, optional

 2078:         The integration method used for the integration of the Lyapunov equation for the covariance.

 2079:         Choose one of 'LSODA', 'RK45', 'RK2' and 'euler'. Default is 'LSODA'.

 2080:     """

 2081: 

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 2179: @cython.cdivision(True)

 2180: @cython.nonecheck(False)

+2181: cdef class SEIR(SIR_type):

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 2182:     """

 2183:     Susceptible, Exposed, Infected, Removed (SEIR)

 2184: 

 2185:     * Ia: asymptomatic

 2186:     * Is: symptomatic

 2187: 

 2188:     To initialise the SEIR class,

 2189: 

 2190:     Parameters

 2191:     ----------

 2192:     parameters: dict

 2193:         Contains the following keys:

 2194: 

 2195:         alpha: float or np.array(M)

 2196:             Fraction of infected who are asymptomatic.

 2197:         beta: float

 2198:             Rate of spread of infection.

 2199:         gIa: float

 2200:             Rate of removal from asymptomatic individuals.

 2201:         gIs: float

 2202:             Rate of removal from symptomatic individuals.

 2203:         fsa: float

 2204:             Fraction by which symptomatic individuals self isolate.

 2205:         gE: float

 2206:             rate of removal from exposed individuals.

 2207:     M: int

 2208:         Number of age groups

 2209:     fi: float numpy.array

 2210:         Fraction of each age group

 2211:     N: int

 2212:         Total population

 2213:     steps: int

 2214:         The number of internal integration steps performed between the observed points (not used in tangent space inference).

 2215:         The minimal is 4, as required by the cubic spline fit used for interpolation.

 2216:         For robustness, set steps to be large, det_method='LSODA', lyapunov_method='LSODA'.

 2217:         For speed, set steps to be 4, det_method='RK2', lyapunov_method='euler'.

 2218:         For a combination of the two, choose something in between.

 2219:     det_method: str, optional

 2220:         The integration method used for deterministic integration.

 2221:         Choose one of 'LSODA', 'RK45', 'RK2' and 'euler'. Default is 'LSODA'.

 2222:     lyapunov_method: str, optional

 2223:         The integration method used for the integration of the Lyapunov equation for the covariance.

 2224:         Choose one of 'LSODA', 'RK45', 'RK2' and 'euler'. Default is 'LSODA'.

 2225:     """

 2226: 

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+2338: cdef class SEAIRQ(SIR_type):

struct __pyx_obj_6pyross_9inference_SEAIRQ {
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 2339:     """

 2340:     Susceptible, Exposed, Asymptomatic and infected, Infected, Removed, Quarantined (SEAIRQ)

 2341: 

 2342:     * Ia: asymptomatic

 2343:     * Is: symptomatic

 2344:     * E: exposed

 2345:     * A: asymptomatic and infectious

 2346:     * Q: quarantined

 2347: 

 2348:     To initialise the SEAIRQ class,

 2349: 

 2350:     Parameters

 2351:     ----------

 2352:     parameters: dict

 2353:         Contains the following keys:

 2354: 

 2355:         alpha: float or np.array(M)

 2356:             Fraction of infected who are asymptomatic.

 2357:         beta: float

 2358:             Rate of spread of infection.

 2359:         gIa: float

 2360:             Rate of removal from asymptomatic individuals.

 2361:         gIs: float

 2362:             Rate of removal from symptomatic individuals.

 2363:         gE: float

 2364:             rate of removal from exposed individuals.

 2365:         gA: float

 2366:             rate of removal from activated individuals.

 2367:         fsa: float

 2368:             fraction by which symptomatic individuals self isolate.

 2369:         tE: float

 2370:             testing rate and contact tracing of exposeds

 2371:         tA: float

 2372:             testing rate and contact tracing of activateds

 2373:         tIa: float

 2374:             testing rate and contact tracing of asymptomatics

 2375:         tIs: float

 2376:             testing rate and contact tracing of symptomatics

 2377:     M: int

 2378:         Number of age groups.

 2379:     fi: float numpy.array

 2380:         Fraction of each age group.

 2381:     N: int

 2382:         Total population.

 2383:     steps: int

 2384:         The number of internal integration steps performed between the observed points (not used in tangent space inference).

 2385:         The minimal is 4, as required by the cubic spline fit used for interpolation.

 2386:         For robustness, set steps to be large, det_method='LSODA', lyapunov_method='LSODA'.

 2387:         For speed, set steps to be 4, det_method='RK2', lyapunov_method='euler'.

 2388:         For a combination of the two, choose something in between.

 2389:     det_method: str, optional

 2390:         The integration method used for deterministic integration.

 2391:         Choose one of 'LSODA', 'RK45', 'RK2' and 'euler'. Default is 'LSODA'.

 2392:     lyapunov_method: str, optional

 2393:         The integration method used for the integration of the Lyapunov equation for the covariance.

 2394:         Choose one of 'LSODA', 'RK45', 'RK2' and 'euler'. Default is 'LSODA'.

 2395:     """

 2396: 

 2397:     cdef:

+2398:         readonly np.ndarray gE, gA, tE, tA, tIa, tIs

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+2499:             double [:] tE=self.tE, tA=self.tE, tIa=self.tIa, tIs=self.tIs, beta=self.beta

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+2500:             double [:] alpha=self.alpha, balpha=1-self.alpha, fi=self.fi

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+2501:             double [:, :, :, :] J = self.J

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+2502:             double [:, :] CM=self.CM

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+2503:         for m in range(M):

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+2504:             J[0, m, 0, m] = -l[m]

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+2505:             J[1, m, 0, m] = l[m]

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+2506:             J[1, m, 1, m] = - gE[m] - tE[m]

    __pyx_t_18 = __pyx_v_m;
    __pyx_t_19 = __pyx_v_m;
    __pyx_t_20 = 1;
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+2507:             J[2, m, 1, m] = gE[m]

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+2508:             J[2, m, 2, m] = - gA[m] - tA[m]

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+2509:             J[3, m, 2, m] = alpha[m]*gA[m]

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+2510:             J[3, m, 3, m] = - gIa[m] - tIa[m]

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+2511:             J[4, m, 2, m] = balpha[m]*gA[m]

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+2512:             J[4, m, 4, m] = -gIs[m] - tIs[m]

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+2513:             J[5, m, 1, m] = tE[m]

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+2514:             J[5, m, 2, m] = tA[m]

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+2515:             J[5, m, 3, m] = tIa[m]

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+2516:             J[5, m, 4, m] = tIs[m]

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+2517:             for n in range(M):

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+2518:                 J[0, m, 2, n] = -s[m]*beta[m]*CM[m, n]/fi[n]

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      __pyx_t_83 = __pyx_v_m;
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+2519:                 J[0, m, 3, n] = -s[m]*beta[m]*CM[m, n]/fi[n]

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      __pyx_t_92 = __pyx_v_m;
      __pyx_t_93 = __pyx_v_m;
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    *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_B.data + __pyx_t_24 * __pyx_v_B.strides[0]) ) + __pyx_t_25 * __pyx_v_B.strides[1]) ) + __pyx_t_26 * __pyx_v_B.strides[2]) ) + __pyx_t_27 * __pyx_v_B.strides[3]) )) = (((*((double *) ( /* dim=0 */ (__pyx_v_l.data + __pyx_t_19 * __pyx_v_l.strides[0]) ))) * (*((double *) ( /* dim=0 */ (__pyx_v_s.data + __pyx_t_20 * __pyx_v_s.strides[0]) )))) + (((*((double *) ( /* dim=0 */ (__pyx_v_gE.data + __pyx_t_21 * __pyx_v_gE.strides[0]) ))) + (*((double *) ( /* dim=0 */ (__pyx_v_tE.data + __pyx_t_22 * __pyx_v_tE.strides[0]) )))) * (*((double *) ( /* dim=0 */ (__pyx_v_e.data + __pyx_t_23 * __pyx_v_e.strides[0]) )))));

+2537:             B[1, m, 2, m] = -gE[m]*e[m]

    __pyx_t_28 = __pyx_v_m;
    __pyx_t_29 = __pyx_v_m;
    __pyx_t_30 = 1;
    __pyx_t_31 = __pyx_v_m;
    __pyx_t_32 = 2;
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+2538:             B[2, m, 2, m] = gE[m]*e[m]+(gA[m]+tA[m])*a[m]

    __pyx_t_34 = __pyx_v_m;
    __pyx_t_35 = __pyx_v_m;
    __pyx_t_36 = __pyx_v_m;
    __pyx_t_37 = __pyx_v_m;
    __pyx_t_38 = __pyx_v_m;
    __pyx_t_39 = 2;
    __pyx_t_40 = __pyx_v_m;
    __pyx_t_41 = 2;
    __pyx_t_42 = __pyx_v_m;
    *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_B.data + __pyx_t_39 * __pyx_v_B.strides[0]) ) + __pyx_t_40 * __pyx_v_B.strides[1]) ) + __pyx_t_41 * __pyx_v_B.strides[2]) ) + __pyx_t_42 * __pyx_v_B.strides[3]) )) = (((*((double *) ( /* dim=0 */ (__pyx_v_gE.data + __pyx_t_34 * __pyx_v_gE.strides[0]) ))) * (*((double *) ( /* dim=0 */ (__pyx_v_e.data + __pyx_t_35 * __pyx_v_e.strides[0]) )))) + (((*((double *) ( /* dim=0 */ (__pyx_v_gA.data + __pyx_t_36 * __pyx_v_gA.strides[0]) ))) + (*((double *) ( /* dim=0 */ (__pyx_v_tA.data + __pyx_t_37 * __pyx_v_tA.strides[0]) )))) * (*((double *) ( /* dim=0 */ (__pyx_v_a.data + __pyx_t_38 * __pyx_v_a.strides[0]) )))));

+2539:             B[2, m, 3, m] = -alpha[m]*gA[m]*a[m]

    __pyx_t_43 = __pyx_v_m;
    __pyx_t_44 = __pyx_v_m;
    __pyx_t_45 = __pyx_v_m;
    __pyx_t_46 = 2;
    __pyx_t_47 = __pyx_v_m;
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+2540:             B[2, m, 4, m] = -balpha[m]*gA[m]*a[m]

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+2541:             B[3, m, 3, m] = alpha[m]*gA[m]*a[m]+(gIa[m]+tIa[m])*Ia[m]

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+2542:             B[4, m, 4, m] = balpha[m]*gA[m]*a[m] + (gIs[m]+tIs[m])*Is[m]

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+2543:             B[1, m, 5, m] = -tE[m]*e[m]

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+2544:             B[2, m, 5, m] = -tA[m]*a[m]

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+2545:             B[3, m, 5, m] = -tIa[m]*Ia[m]

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+2546:             B[4, m, 5, m] = -tIs[m]*Is[m]

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+2547:             B[5, m, 5, m] = tE[m]*e[m]+tA[m]*a[m]+tIa[m]*Ia[m]+tIs[m]*Is[m]

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 2549: 

 2550: 

 2551: @cython.wraparound(False)

 2552: @cython.boundscheck(False)

 2553: @cython.cdivision(True)

 2554: @cython.nonecheck(False)

+2555: cdef class SEAIRQ_testing(SIR_type):

struct __pyx_obj_6pyross_9inference_SEAIRQ_testing {
  struct __pyx_obj_6pyross_9inference_SIR_type __pyx_base;
  PyArrayObject *gE;
  PyArrayObject *gA;
  PyArrayObject *ars;
  PyArrayObject *kapE;
  PyObject *testRate;
};
/* … */
struct __pyx_vtabstruct_6pyross_9inference_SEAIRQ_testing {
  struct __pyx_vtabstruct_6pyross_9inference_SIR_type __pyx_base;
  PyObject *(*fill_lambdas)(struct __pyx_obj_6pyross_9inference_SEAIRQ_testing *, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice);
  PyObject *(*jacobian)(struct __pyx_obj_6pyross_9inference_SEAIRQ_testing *, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice);
  PyObject *(*noise_correlation)(struct __pyx_obj_6pyross_9inference_SEAIRQ_testing *, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice);
};
static struct __pyx_vtabstruct_6pyross_9inference_SEAIRQ_testing *__pyx_vtabptr_6pyross_9inference_SEAIRQ_testing;

 2556:     """

 2557:     Susceptible, Exposed, Asymptomatic and infected, Infected, Removed, Quarantined (SEAIRQ)

 2558:     Ia: asymptomatic

 2559:     Is: symptomatic

 2560:     A : Asymptomatic and infectious

 2561: 

 2562:     * Ia: asymptomatic

 2563:     * Is: symptomatic

 2564:     * E: exposed

 2565:     * A: asymptomatic and infectious

 2566:     * Q: quarantined

 2567: 

 2568:     To initialise the SEAIRQ class,

 2569: 

 2570:     Parameters

 2571:     ----------

 2572:     parameters: dict

 2573:         Contains the following keys:

 2574: 

 2575:         alpha: float or np.array(M)

 2576:             Fraction of infected who are asymptomatic.

 2577:         beta: float

 2578:             Rate of spread of infection.

 2579:         gIa: float

 2580:             Rate of removal from asymptomatic individuals.

 2581:         gIs: float

 2582:             Rate of removal from symptomatic individuals.

 2583:         gE: float

 2584:             rate of removal from exposed individuals.

 2585:         gA: float

 2586:             rate of removal from activated individuals.

 2587:         fsa: float

 2588:             fraction by which symptomatic individuals self isolate.

 2589:         ars : float

 2590:             fraction of population admissible for random and symptomatic tests

 2591:         kapE : float

 2592:             fraction of positive tests for exposed individuals

 2593:     testRate: python function

 2594:         number of tests per day and age group

 2595:     M: int

 2596:         Number of age groups.

 2597:     fi: float numpy.array

 2598:         Fraction of each age group.

 2599:     N: int

 2600:         Total population.

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+2735:             tIa = TR[m]*ars[m]*t0/N

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+2741:                 J[0, m, 4, n] = -s[m]*beta[m]*CM[m, n]*fsa[n]/fi[n]

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+2742:                 J[1, m, 2, n] = s[m]*beta[m]*CM[m, n]/fi[n]

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+2743:                 J[1, m, 3, n] = s[m]*beta[m]*CM[m, n]/fi[n]

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+2744:                 J[1, m, 4, n] = s[m]*beta[m]*CM[m, n]*fsa[n]/fi[n]

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    *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_84 * __pyx_v_J.strides[0]) ) + __pyx_t_85 * __pyx_v_J.strides[1]) ) + __pyx_t_86 * __pyx_v_J.strides[2]) ) + __pyx_t_87 * __pyx_v_J.strides[3]) )) = (-(*((double *) ( /* dim=0 */ (__pyx_v_l.data + __pyx_t_83 * __pyx_v_l.strides[0]) ))));

+2746:             J[1, m, 0, m] = l[m]

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    __pyx_t_89 = 1;
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    *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_89 * __pyx_v_J.strides[0]) ) + __pyx_t_90 * __pyx_v_J.strides[1]) ) + __pyx_t_91 * __pyx_v_J.strides[2]) ) + __pyx_t_92 * __pyx_v_J.strides[3]) )) = (*((double *) ( /* dim=0 */ (__pyx_v_l.data + __pyx_t_88 * __pyx_v_l.strides[0]) )));

+2747:             J[1, m, 1, m] = - gE[m] - tE

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    __pyx_t_94 = 1;
    __pyx_t_95 = __pyx_v_m;
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    *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_94 * __pyx_v_J.strides[0]) ) + __pyx_t_95 * __pyx_v_J.strides[1]) ) + __pyx_t_96 * __pyx_v_J.strides[2]) ) + __pyx_t_97 * __pyx_v_J.strides[3]) )) = ((-(*((double *) ( /* dim=0 */ (__pyx_v_gE.data + __pyx_t_93 * __pyx_v_gE.strides[0]) )))) - __pyx_v_tE);

+2748:             J[1, m, 4, m] += (1-ars[m])*tE*t0*e[m]

    __pyx_t_98 = __pyx_v_m;
    __pyx_t_99 = __pyx_v_m;
    __pyx_t_100 = 1;
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    *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_100 * __pyx_v_J.strides[0]) ) + __pyx_t_101 * __pyx_v_J.strides[1]) ) + __pyx_t_102 * __pyx_v_J.strides[2]) ) + __pyx_t_103 * __pyx_v_J.strides[3]) )) += ((((1.0 - (*((double *) ( /* dim=0 */ (__pyx_v_ars.data + __pyx_t_98 * __pyx_v_ars.strides[0]) )))) * __pyx_v_tE) * __pyx_v_t0) * (*((double *) ( /* dim=0 */ (__pyx_v_e.data + __pyx_t_99 * __pyx_v_e.strides[0]) ))));

+2749:             J[1, m, 5, m] = -ars[m]*tE*t0*e[m]

    __pyx_t_104 = __pyx_v_m;
    __pyx_t_105 = __pyx_v_m;
    __pyx_t_106 = 1;
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    __pyx_t_108 = 5;
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    *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_106 * __pyx_v_J.strides[0]) ) + __pyx_t_107 * __pyx_v_J.strides[1]) ) + __pyx_t_108 * __pyx_v_J.strides[2]) ) + __pyx_t_109 * __pyx_v_J.strides[3]) )) = ((((-(*((double *) ( /* dim=0 */ (__pyx_v_ars.data + __pyx_t_104 * __pyx_v_ars.strides[0]) )))) * __pyx_v_tE) * __pyx_v_t0) * (*((double *) ( /* dim=0 */ (__pyx_v_e.data + __pyx_t_105 * __pyx_v_e.strides[0]) ))));

+2750:             J[2, m, 1, m] = gE[m]

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+2751:             J[2, m, 2, m] = - gA[m] - tA

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+2752:             J[2, m, 4, m] = (1-ars[m])*tA*t0*a[m]

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    __pyx_t_121 = __pyx_v_m;
    __pyx_t_122 = 2;
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+2753:             J[2, m, 5, m] = - ars[m]*tA*t0*a[m]

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    __pyx_t_127 = __pyx_v_m;
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+2754:             J[3, m, 2, m] = alpha[m]*gA[m]

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    __pyx_t_133 = __pyx_v_m;
    __pyx_t_134 = 3;
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+2755:             J[3, m, 3, m] = - gIa[m] - tIa

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+2756:             J[3, m, 4, m] = (1-ars[m])*tIa*t0*Ia[m]

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+2757:             J[3, m, 5, m] = - ars[m]*tIa*t0*Ia[m]

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+2758:             J[4, m, 2, m] = balpha[m]*gA[m]

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    __pyx_t_156 = __pyx_v_m;
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    *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_157 * __pyx_v_J.strides[0]) ) + __pyx_t_158 * __pyx_v_J.strides[1]) ) + __pyx_t_159 * __pyx_v_J.strides[2]) ) + __pyx_t_160 * __pyx_v_J.strides[3]) )) = ((*((double *) ( /* dim=0 */ (__pyx_v_balpha.data + __pyx_t_155 * __pyx_v_balpha.strides[0]) ))) * (*((double *) ( /* dim=0 */ (__pyx_v_gA.data + __pyx_t_156 * __pyx_v_gA.strides[0]) ))));

+2759:             J[4, m, 4, m] = - gIs[m] - tIs + (1-ars[m])*tIs*t0*Is[m]

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+2760:             J[4, m, 5, m] = - ars[m]*tIs*t0*Is[m]

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+2761:             J[5, m, 1, m] = tE

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    *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_174 * __pyx_v_J.strides[0]) ) + __pyx_t_175 * __pyx_v_J.strides[1]) ) + __pyx_t_176 * __pyx_v_J.strides[2]) ) + __pyx_t_177 * __pyx_v_J.strides[3]) )) = __pyx_v_tE;

+2762:             J[5, m, 2, m] = tA

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    __pyx_t_180 = 2;
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+2763:             J[5, m, 3, m] = tIa

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    __pyx_t_184 = 3;
    __pyx_t_185 = __pyx_v_m;
    *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_182 * __pyx_v_J.strides[0]) ) + __pyx_t_183 * __pyx_v_J.strides[1]) ) + __pyx_t_184 * __pyx_v_J.strides[2]) ) + __pyx_t_185 * __pyx_v_J.strides[3]) )) = __pyx_v_tIa;

+2764:             J[5, m, 4, m] = tIs - (1-ars[m])*t0*(tE*e[m]+tA*a[m]+tIa*Ia[m]+tIs*Is[m])

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    __pyx_t_194 = __pyx_v_m;
    *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_191 * __pyx_v_J.strides[0]) ) + __pyx_t_192 * __pyx_v_J.strides[1]) ) + __pyx_t_193 * __pyx_v_J.strides[2]) ) + __pyx_t_194 * __pyx_v_J.strides[3]) )) = (__pyx_v_tIs - (((1.0 - (*((double *) ( /* dim=0 */ (__pyx_v_ars.data + __pyx_t_186 * __pyx_v_ars.strides[0]) )))) * __pyx_v_t0) * ((((__pyx_v_tE * (*((double *) ( /* dim=0 */ (__pyx_v_e.data + __pyx_t_187 * __pyx_v_e.strides[0]) )))) + (__pyx_v_tA * (*((double *) ( /* dim=0 */ (__pyx_v_a.data + __pyx_t_188 * __pyx_v_a.strides[0]) ))))) + (__pyx_v_tIa * (*((double *) ( /* dim=0 */ (__pyx_v_Ia.data + __pyx_t_189 * __pyx_v_Ia.strides[0]) ))))) + (__pyx_v_tIs * (*((double *) ( /* dim=0 */ (__pyx_v_Is.data + __pyx_t_190 * __pyx_v_Is.strides[0]) )))))));

+2765:             J[5, m, 5, m] = ars[m]*t0*(tE*e[m]+tA*a[m]+tIa*Ia[m]+tIs*Is[m])

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+2766:         self.J_mat = self.J.reshape((dim, dim))

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+2787:             B[1, m, 1, m] = l[m]*s[m] + (gE[m]+tE)*e[m]

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+2788:             B[1, m, 2, m] = -gE[m]*e[m]

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+2789:             B[2, m, 2, m] = gE[m]*e[m]+(gA[m]+tA)*a[m]

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+2791:             B[2, m, 4, m] = -balpha[m]*gA[m]*a[m]

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+2792:             B[3, m, 3, m] = alpha[m]*gA[m]*a[m]+(gIa[m]+tIa)*Ia[m]

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+2796:             B[3, m, 5, m] = -tIa*Ia[m]

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+2797:             B[4, m, 5, m] = -tIs*Is[m]

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+2798:             B[5, m, 5, m] = tE*e[m]+tA*a[m]+tIa*Ia[m]+tIs*Is[m]

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 2800: 

 2801: 

 2802: 

 2803: 

 2804: @cython.wraparound(False)

 2805: @cython.boundscheck(False)

 2806: @cython.cdivision(True)

 2807: @cython.nonecheck(False)

+2808: cdef class Spp(SIR_type):

struct __pyx_obj_6pyross_9inference_Spp {
  struct __pyx_obj_6pyross_9inference_SIR_type __pyx_base;
  PyArrayObject *constant_terms;
  PyArrayObject *linear_terms;
  PyArrayObject *infection_terms;
  PyArrayObject *parameters;
  PyObject *param_keys;
  struct __pyx_obj_6pyross_13deterministic_Spp *det_model;
};
/* … */
struct __pyx_vtabstruct_6pyross_9inference_Spp {
  struct __pyx_vtabstruct_6pyross_9inference_SIR_type __pyx_base;
  PyObject *(*fill_lambdas)(struct __pyx_obj_6pyross_9inference_Spp *, __Pyx_memviewslice, __Pyx_memviewslice);
  PyObject *(*jacobian)(struct __pyx_obj_6pyross_9inference_Spp *, __Pyx_memviewslice, __Pyx_memviewslice);
  PyObject *(*noise_correlation)(struct __pyx_obj_6pyross_9inference_Spp *, __Pyx_memviewslice, __Pyx_memviewslice);
  PyObject *(*obtain_time_evol_op_2)(struct __pyx_obj_6pyross_9inference_Spp *, __Pyx_memviewslice, __Pyx_memviewslice, double, double, PyObject *, PyObject *);
};
static struct __pyx_vtabstruct_6pyross_9inference_Spp *__pyx_vtabptr_6pyross_9inference_Spp;

 2809:     """User-defined epidemic model.

 2810: 

 2811:     To initialise the Spp model,

 2812: 

 2813:     Parameters

 2814:     ----------

 2815:     model_spec: dict

 2816:         A dictionary specifying the model. See `Examples`.

 2817:     parameters: dict

 2818:         A dictionary containing the model parameters.

 2819:         All parameters can be float if not age-dependent, and np.array(M,) if age-dependent

 2820:     M: int

 2821:         Number of age groups.

 2822:     fi: np.array(M) or list

 2823:         Fraction of each age group.

 2824:     N: int

 2825:         Total population.

 2826:     steps: int

 2827:         The number of internal integration steps performed between the observed points (not used in tangent space inference).

 2828:         The minimal is 4, as required by the cubic spline fit used for interpolation.

 2829:         For robustness, set steps to be large, det_method='LSODA', lyapunov_method='LSODA'.

 2830:         For speed, set steps to be 4, det_method='RK2', lyapunov_method='euler'.

 2831:         For a combination of the two, choose something in between.

 2832:     det_method: str, optional

 2833:         The integration method used for deterministic integration.

 2834:         Choose one of 'LSODA', 'RK45', 'RK2' and 'euler'. Default is 'LSODA'.

 2835:     lyapunov_method: str, optional

 2836:         The integration method used for the integration of the Lyapunov equation for the covariance.

 2837:         Choose one of 'LSODA', 'RK45', 'RK2' and 'euler'. Default is 'LSODA'.

 2838: 

 2839: 

 2840:     See `SIR_type` for a table of all the methods

 2841: 

 2842:     Examples

 2843:     --------

 2844:     An example of model_spec and parameters for SIR class with a constant influx

 2845: 

 2846:     >>> model_spec = {

 2847:             "classes" : ["S", "I"],

 2848:             "S" : {

 2849:                 "constant"  : [ ["k"] ],

 2850:                 "infection" : [ ["I", "-beta"] ]

 2851:             },

 2852:             "I" : {

 2853:                 "linear"    : [ ["I", "-gamma"] ],

 2854:                 "infection" : [ ["I", "beta"] ]

 2855:             }

 2856:         }

 2857:     >>> parameters = {

 2858:             'beta': 0.1,

 2859:             'gamma': 0.1,

 2860:             'k': 1,

 2861:         }

 2862:     """

 2863: 

 2864:     cdef:

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__pyx_t_7.shape[0] = __pyx_v_parameters.shape[1];
__pyx_t_7.strides[0] = __pyx_v_parameters.strides[1];
    __pyx_t_7.suboffsets[0] = -1;

__PYX_XDEC_MEMVIEW(&__pyx_v_rate, 1);
      __pyx_v_rate = __pyx_t_7;
      __pyx_t_7.memview = NULL;
      __pyx_t_7.data = NULL;

+3036:                 for m in range(M):

      __pyx_t_17 = __pyx_v_M;
      __pyx_t_18 = __pyx_t_17;
      for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {
        __pyx_v_m = __pyx_t_19;

+3037:                     B[class_index, m, class_index, m] += rate[m]/N

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        __pyx_t_21 = __pyx_v_class_index;
        __pyx_t_22 = __pyx_v_m;
        __pyx_t_23 = __pyx_v_class_index;
        __pyx_t_24 = __pyx_v_m;
        *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_B.data + __pyx_t_21 * __pyx_v_B.strides[0]) ) + __pyx_t_22 * __pyx_v_B.strides[1]) ) + __pyx_t_23 * __pyx_v_B.strides[2]) ) + __pyx_t_24 * __pyx_v_B.strides[3]) )) += ((*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_20 * __pyx_v_rate.strides[0]) ))) / __pyx_v_N);

+3038:                     B[nClass-1, m, nClass-1, m] += rate[m]/N

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        __pyx_t_26 = (__pyx_v_nClass - 1);
        __pyx_t_27 = __pyx_v_m;
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        __pyx_t_29 = __pyx_v_m;
        *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_B.data + __pyx_t_26 * __pyx_v_B.strides[0]) ) + __pyx_t_27 * __pyx_v_B.strides[1]) ) + __pyx_t_28 * __pyx_v_B.strides[2]) ) + __pyx_t_29 * __pyx_v_B.strides[3]) )) += ((*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_25 * __pyx_v_rate.strides[0]) ))) / __pyx_v_N);
      }
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 3039: 

+3040:         for i in range(infection_terms.shape[0]):

  __pyx_t_1 = (__pyx_v_infection_terms.shape[0]);
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    __pyx_v_i = __pyx_t_12;

+3041:             product_index = infection_terms[i, 2]

    __pyx_t_30 = __pyx_v_i;
    __pyx_t_31 = 2;
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+3042:             infective_index = infection_terms[i, 1]

    __pyx_t_32 = __pyx_v_i;
    __pyx_t_33 = 1;
    __pyx_v_infective_index = (*((int *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_infection_terms.data + __pyx_t_32 * __pyx_v_infection_terms.strides[0]) ) + __pyx_t_33 * __pyx_v_infection_terms.strides[1]) )));

+3043:             rate_index = infection_terms[i, 0]

    __pyx_t_34 = __pyx_v_i;
    __pyx_t_35 = 0;
    __pyx_v_rate_index = (*((int *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_infection_terms.data + __pyx_t_34 * __pyx_v_infection_terms.strides[0]) ) + __pyx_t_35 * __pyx_v_infection_terms.strides[1]) )));

+3044:             rate = parameters[rate_index]

    __pyx_t_7.data = __pyx_v_parameters.data;
    __pyx_t_7.memview = __pyx_v_parameters.memview;
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__PYX_XDEC_MEMVIEW(&__pyx_v_rate, 1);
    __pyx_v_rate = __pyx_t_7;
    __pyx_t_7.memview = NULL;
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+3045:             for m in range(M):

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    for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {
      __pyx_v_m = __pyx_t_19;

+3046:                 B[S_index, m, S_index, m] += rate[m]*l[i, m]*s[m]

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      __pyx_t_37 = __pyx_v_i;
      __pyx_t_38 = __pyx_v_m;
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      __pyx_t_42 = __pyx_v_S_index;
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+3047:                 if product_index>-1:

      __pyx_t_10 = ((__pyx_v_product_index > -1L) != 0);
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/* … */
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+3048:                     B[S_index, m, product_index, m] -=  rate[m]*l[i, m]*s[m]

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+3049:                     B[product_index, m, product_index, m] += rate[m]*l[i, m]*s[m]

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+3050:                     B[product_index, m, S_index, m] -= rate[m]*l[i, m]*s[m]

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 3051: 

+3052:         for i in range(linear_terms.shape[0]):

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+3053:             product_index = linear_terms[i, 2]

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+3054:             reagent_index = linear_terms[i, 1]

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+3055:             reagent = x[reagent_index*M:(reagent_index+1)*M]

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    __pyx_t_7.data = NULL;

+3056:             rate_index = linear_terms[i, 0]

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+3057:             rate = parameters[rate_index]

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    __pyx_t_7.memview = __pyx_v_parameters.memview;
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__pyx_t_7.strides[0] = __pyx_v_parameters.strides[1];
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__PYX_XDEC_MEMVIEW(&__pyx_v_rate, 1);
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    __pyx_t_7.data = NULL;

+3058:             for m in range(M): # only fill in the upper triangular form

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    for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {
      __pyx_v_m = __pyx_t_19;

+3059:                 B[reagent_index, m, reagent_index, m] += rate[m]*reagent[m]

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+3060:                 if product_index>-1:

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/* … */
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+3061:                     B[product_index, m, product_index, m] += rate[m]*reagent[m]

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+3062:                     B[reagent_index, m, product_index, m] += -rate[m]*reagent[m]

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+3063:                     B[product_index, m, reagent_index, m] += -rate[m]*reagent[m]

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 3387: 

 3388: 

 3389: @cython.wraparound(False)

 3390: @cython.boundscheck(False)

 3391: @cython.cdivision(True)

 3392: @cython.nonecheck(False)

+3393: cdef class SppQ(SIR_type):

struct __pyx_obj_6pyross_9inference_SppQ {
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 3394:     """User-defined epidemic model with quarantine stage.

 3395: 

 3396:     To initialise the SppQ model,

 3397: 

 3398:     Parameters

 3399:     ----------

 3400:     model_spec: dict

 3401:         A dictionary specifying the model. See `Examples`.

 3402:     parameters: dict

 3403:         A dictionary containing the model parameters.

 3404:         All parameters can be float if not age-dependent, and np.array(M,) if age-dependent

 3405:     testRate: python function

 3406:         number of tests per day and age group

 3407:     M: int

 3408:         Number of age groups.

 3409:     fi: np.array(M) or list

 3410:         Fraction of each age group.

 3411:     N: int

 3412:         Total population.

 3413:     steps: int

 3414:         The number of internal integration steps performed between the observed points (not used in tangent space inference).

 3415:         The minimal is 4, as required by the cubic spline fit used for interpolation.

 3416:         For robustness, set steps to be large, det_method='LSODA', lyapunov_method='LSODA'.

 3417:         For speed, set steps to be 4, det_method='RK2', lyapunov_method='euler'.

 3418:         For a combination of the two, choose something in between.

 3419:     det_method: str, optional

 3420:         The integration method used for deterministic integration.

 3421:         Choose one of 'LSODA', 'RK45', 'RK2' and 'euler'. Default is 'LSODA'.

 3422:     lyapunov_method: str, optional

 3423:         The integration method used for the integration of the Lyapunov equation for the covariance.

 3424:         Choose one of 'LSODA', 'RK45', 'RK2' and 'euler'. Default is 'LSODA'.

 3425: 

 3426: 

 3427:     See `SIR_type` for a table of all the methods

 3428: 

 3429:     Examples

 3430:     --------

 3431:     An example of model_spec and parameters for SIR class with a constant influx,

 3432:     random testing (without false positives/negatives), and quarantine

 3433: 

 3434:     >>> model_spec = {

 3435:             "classes" : ["S", "I"],

 3436:             "S" : {

 3437:                 "infection" : [ ["I", "-beta"] ]

 3438:             },

 3439:             "I" : {

 3440:                 "linear"    : [ ["I", "-gamma"] ],

 3441:                 "infection" : [ ["I", "beta"] ]

 3442:             },

 3443:             "test_pos"  : [ "p_falsepos", "p_truepos", "p_falsepos"] ,

 3444:             "test_freq" : [ "tf", "tf", "tf"]

 3445:         }

 3446:     >>> parameters = {

 3447:             'beta': 0.1,

 3448:             'gamma': 0.1,

 3449:             'p_falsepos': 0

 3450:             'p_truepos': 1

 3451:             'tf': 1

 3452:         }

 3453:     """

 3454: 

 3455:     cdef:

+3456:         readonly np.ndarray constant_terms, linear_terms, infection_terms, test_pos, test_freq

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  double __pyx_v_term2;
  double __pyx_v_term3;
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 3642:         cdef:

+3643:             Py_ssize_t i, m, n, M=self.M, dim=self.dim

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  __pyx_v_M = __pyx_t_1;
  __pyx_t_1 = __pyx_v_self->__pyx_base.dim;
  __pyx_v_dim = __pyx_t_1;

+3644:             Py_ssize_t nClass=self.nClass, nClassU=self.nClassU, nClassUwoN=self.nClassUwoN

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  __pyx_v_nClass = __pyx_t_1;
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+3645:             Py_ssize_t rate_index, infective_index, product_index, reagent_index, S_index=self.class_index_dict['S']

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+3646:             double [:, :, :, :] J = self.J

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+3647:             double [:, :] CM=self.CM

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+3648:             double [:, :] parameters=self.parameters

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+3649:             int [:, :] linear_terms=self.linear_terms, infection_terms=self.infection_terms

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  __pyx_v_linear_terms = __pyx_t_5;
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  __pyx_t_5.data = NULL;
  __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dsds_int(((PyObject *)__pyx_v_self->infection_terms), PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 3649, __pyx_L1_error)
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  __pyx_t_5.data = NULL;

+3650:             int [:] test_pos=self.test_pos

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+3651:             int [:] test_freq=self.test_freq

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 3652:             double [:] rate

 3653:             double term, term2, term3

+3654:             double [:] fi=self.fi

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 3655: 

 3656:         # infection terms (no infection terms in Q classes, perfect quarantine)

+3657:         for i in range(infection_terms.shape[0]):

  __pyx_t_1 = (__pyx_v_infection_terms.shape[0]);
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    __pyx_v_i = __pyx_t_9;

+3658:             product_index = infection_terms[i, 2]

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    __pyx_v_product_index = (*((int *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_infection_terms.data + __pyx_t_10 * __pyx_v_infection_terms.strides[0]) ) + __pyx_t_11 * __pyx_v_infection_terms.strides[1]) )));

+3659:             infective_index = infection_terms[i, 1]

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+3660:             rate_index = infection_terms[i, 0]

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+3661:             rate = parameters[rate_index]

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__pyx_t_7.strides[0] = __pyx_v_parameters.strides[1];
    __pyx_t_7.suboffsets[0] = -1;

__PYX_XDEC_MEMVIEW(&__pyx_v_rate, 1);
    __pyx_v_rate = __pyx_t_7;
    __pyx_t_7.memview = NULL;
    __pyx_t_7.data = NULL;

+3662:             for m in range(M):

    __pyx_t_16 = __pyx_v_M;
    __pyx_t_17 = __pyx_t_16;
    for (__pyx_t_18 = 0; __pyx_t_18 < __pyx_t_17; __pyx_t_18+=1) {
      __pyx_v_m = __pyx_t_18;

+3663:                 J[S_index, m, S_index, m] -= rate[m]*l[i, m]

      __pyx_t_19 = __pyx_v_m;
      __pyx_t_20 = __pyx_v_i;
      __pyx_t_21 = __pyx_v_m;
      __pyx_t_22 = __pyx_v_S_index;
      __pyx_t_23 = __pyx_v_m;
      __pyx_t_24 = __pyx_v_S_index;
      __pyx_t_25 = __pyx_v_m;
      *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_22 * __pyx_v_J.strides[0]) ) + __pyx_t_23 * __pyx_v_J.strides[1]) ) + __pyx_t_24 * __pyx_v_J.strides[2]) ) + __pyx_t_25 * __pyx_v_J.strides[3]) )) -= ((*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_19 * __pyx_v_rate.strides[0]) ))) * (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_l.data + __pyx_t_20 * __pyx_v_l.strides[0]) ) + __pyx_t_21 * __pyx_v_l.strides[1]) ))));

+3664:                 if product_index>-1:

      __pyx_t_26 = ((__pyx_v_product_index > -1L) != 0);
      if (__pyx_t_26) {
/* … */
      }

+3665:                     J[product_index, m, S_index, m] += rate[m]*l[i, m]

        __pyx_t_27 = __pyx_v_m;
        __pyx_t_28 = __pyx_v_i;
        __pyx_t_29 = __pyx_v_m;
        __pyx_t_30 = __pyx_v_product_index;
        __pyx_t_31 = __pyx_v_m;
        __pyx_t_32 = __pyx_v_S_index;
        __pyx_t_33 = __pyx_v_m;
        *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_30 * __pyx_v_J.strides[0]) ) + __pyx_t_31 * __pyx_v_J.strides[1]) ) + __pyx_t_32 * __pyx_v_J.strides[2]) ) + __pyx_t_33 * __pyx_v_J.strides[3]) )) += ((*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_27 * __pyx_v_rate.strides[0]) ))) * (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_l.data + __pyx_t_28 * __pyx_v_l.strides[0]) ) + __pyx_t_29 * __pyx_v_l.strides[1]) ))));

+3666:                 for n in range(M):

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      for (__pyx_t_36 = 0; __pyx_t_36 < __pyx_t_35; __pyx_t_36+=1) {
        __pyx_v_n = __pyx_t_36;

+3667:                     J[S_index, m, infective_index, n] -= x[S_index*M+m]*rate[m]*CM[m, n]/fi[n]

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        __pyx_t_40 = __pyx_v_n;
        __pyx_t_41 = __pyx_v_n;
        __pyx_t_42 = __pyx_v_S_index;
        __pyx_t_43 = __pyx_v_m;
        __pyx_t_44 = __pyx_v_infective_index;
        __pyx_t_45 = __pyx_v_n;
        *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_42 * __pyx_v_J.strides[0]) ) + __pyx_t_43 * __pyx_v_J.strides[1]) ) + __pyx_t_44 * __pyx_v_J.strides[2]) ) + __pyx_t_45 * __pyx_v_J.strides[3]) )) -= ((((*((double *) ( /* dim=0 */ (__pyx_v_x.data + __pyx_t_37 * __pyx_v_x.strides[0]) ))) * (*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_38 * __pyx_v_rate.strides[0]) )))) * (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_CM.data + __pyx_t_39 * __pyx_v_CM.strides[0]) ) + __pyx_t_40 * __pyx_v_CM.strides[1]) )))) / (*((double *) ( /* dim=0 */ (__pyx_v_fi.data + __pyx_t_41 * __pyx_v_fi.strides[0]) ))));

+3668:                     if product_index>-1:

        __pyx_t_26 = ((__pyx_v_product_index > -1L) != 0);
        if (__pyx_t_26) {
/* … */
        }
      }
    }
  }

+3669:                         J[product_index, m, infective_index, n] += x[S_index*M+m]*rate[m]*CM[m, n]/fi[n]

          __pyx_t_46 = ((__pyx_v_S_index * __pyx_v_M) + __pyx_v_m);
          __pyx_t_47 = __pyx_v_m;
          __pyx_t_48 = __pyx_v_m;
          __pyx_t_49 = __pyx_v_n;
          __pyx_t_50 = __pyx_v_n;
          __pyx_t_51 = __pyx_v_product_index;
          __pyx_t_52 = __pyx_v_m;
          __pyx_t_53 = __pyx_v_infective_index;
          __pyx_t_54 = __pyx_v_n;
          *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_51 * __pyx_v_J.strides[0]) ) + __pyx_t_52 * __pyx_v_J.strides[1]) ) + __pyx_t_53 * __pyx_v_J.strides[2]) ) + __pyx_t_54 * __pyx_v_J.strides[3]) )) += ((((*((double *) ( /* dim=0 */ (__pyx_v_x.data + __pyx_t_46 * __pyx_v_x.strides[0]) ))) * (*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_47 * __pyx_v_rate.strides[0]) )))) * (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_CM.data + __pyx_t_48 * __pyx_v_CM.strides[0]) ) + __pyx_t_49 * __pyx_v_CM.strides[1]) )))) / (*((double *) ( /* dim=0 */ (__pyx_v_fi.data + __pyx_t_50 * __pyx_v_fi.strides[0]) ))));

 3670:         # linear terms

+3671:         for i in range(linear_terms.shape[0]):

  __pyx_t_1 = (__pyx_v_linear_terms.shape[0]);
  __pyx_t_8 = __pyx_t_1;
  for (__pyx_t_9 = 0; __pyx_t_9 < __pyx_t_8; __pyx_t_9+=1) {
    __pyx_v_i = __pyx_t_9;

+3672:             product_index = linear_terms[i, 2]

    __pyx_t_55 = __pyx_v_i;
    __pyx_t_56 = 2;
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+3673:             reagent_index = linear_terms[i, 1]

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    __pyx_t_58 = 1;
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+3674:             rate_index = linear_terms[i, 0]

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+3675:             rate = parameters[rate_index]

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__pyx_t_7.strides[0] = __pyx_v_parameters.strides[1];
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__PYX_XDEC_MEMVIEW(&__pyx_v_rate, 1);
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    __pyx_t_7.memview = NULL;
    __pyx_t_7.data = NULL;

+3676:             for m in range(M):

    __pyx_t_16 = __pyx_v_M;
    __pyx_t_17 = __pyx_t_16;
    for (__pyx_t_18 = 0; __pyx_t_18 < __pyx_t_17; __pyx_t_18+=1) {
      __pyx_v_m = __pyx_t_18;

+3677:                 J[reagent_index, m, reagent_index, m] -= rate[m]

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      __pyx_t_62 = __pyx_v_reagent_index;
      __pyx_t_63 = __pyx_v_m;
      __pyx_t_64 = __pyx_v_reagent_index;
      __pyx_t_65 = __pyx_v_m;
      *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_62 * __pyx_v_J.strides[0]) ) + __pyx_t_63 * __pyx_v_J.strides[1]) ) + __pyx_t_64 * __pyx_v_J.strides[2]) ) + __pyx_t_65 * __pyx_v_J.strides[3]) )) -= (*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_61 * __pyx_v_rate.strides[0]) )));

+3678:                 J[reagent_index + nClassU, m, reagent_index + nClassU, m] -= rate[m]

      __pyx_t_66 = __pyx_v_m;
      __pyx_t_67 = (__pyx_v_reagent_index + __pyx_v_nClassU);
      __pyx_t_68 = __pyx_v_m;
      __pyx_t_69 = (__pyx_v_reagent_index + __pyx_v_nClassU);
      __pyx_t_70 = __pyx_v_m;
      *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_67 * __pyx_v_J.strides[0]) ) + __pyx_t_68 * __pyx_v_J.strides[1]) ) + __pyx_t_69 * __pyx_v_J.strides[2]) ) + __pyx_t_70 * __pyx_v_J.strides[3]) )) -= (*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_66 * __pyx_v_rate.strides[0]) )));

+3679:                 if product_index>-1:

      __pyx_t_26 = ((__pyx_v_product_index > -1L) != 0);
      if (__pyx_t_26) {
/* … */
      }
    }
  }

+3680:                     J[product_index, m, reagent_index, m] += rate[m]

        __pyx_t_71 = __pyx_v_m;
        __pyx_t_72 = __pyx_v_product_index;
        __pyx_t_73 = __pyx_v_m;
        __pyx_t_74 = __pyx_v_reagent_index;
        __pyx_t_75 = __pyx_v_m;
        *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_72 * __pyx_v_J.strides[0]) ) + __pyx_t_73 * __pyx_v_J.strides[1]) ) + __pyx_t_74 * __pyx_v_J.strides[2]) ) + __pyx_t_75 * __pyx_v_J.strides[3]) )) += (*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_71 * __pyx_v_rate.strides[0]) )));

+3681:                     J[product_index + nClassU, m, reagent_index + nClassU, m] += rate[m]

        __pyx_t_76 = __pyx_v_m;
        __pyx_t_77 = (__pyx_v_product_index + __pyx_v_nClassU);
        __pyx_t_78 = __pyx_v_m;
        __pyx_t_79 = (__pyx_v_reagent_index + __pyx_v_nClassU);
        __pyx_t_80 = __pyx_v_m;
        *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_77 * __pyx_v_J.strides[0]) ) + __pyx_t_78 * __pyx_v_J.strides[1]) ) + __pyx_t_79 * __pyx_v_J.strides[2]) ) + __pyx_t_80 * __pyx_v_J.strides[3]) )) += (*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_76 * __pyx_v_rate.strides[0]) )));

 3682:         # quarantining terms

+3683:         for m in range(M):

  __pyx_t_1 = __pyx_v_M;
  __pyx_t_8 = __pyx_t_1;
  for (__pyx_t_9 = 0; __pyx_t_9 < __pyx_t_8; __pyx_t_9+=1) {
    __pyx_v_m = __pyx_t_9;

+3684:             for i in range(nClassUwoN):

    __pyx_t_16 = __pyx_v_nClassUwoN;
    __pyx_t_17 = __pyx_t_16;
    for (__pyx_t_18 = 0; __pyx_t_18 < __pyx_t_17; __pyx_t_18+=1) {
      __pyx_v_i = __pyx_t_18;

+3685:                 term = tau0 * parameters[test_freq[i], m] * parameters[test_pos[i], m]

      __pyx_t_81 = __pyx_v_i;
      __pyx_t_82 = (*((int *) ( /* dim=0 */ (__pyx_v_test_freq.data + __pyx_t_81 * __pyx_v_test_freq.strides[0]) )));
      __pyx_t_83 = __pyx_v_m;
      __pyx_t_84 = __pyx_v_i;
      __pyx_t_85 = (*((int *) ( /* dim=0 */ (__pyx_v_test_pos.data + __pyx_t_84 * __pyx_v_test_pos.strides[0]) )));
      __pyx_t_86 = __pyx_v_m;
      __pyx_v_term = ((__pyx_v_tau0 * (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_parameters.data + __pyx_t_82 * __pyx_v_parameters.strides[0]) ) + __pyx_t_83 * __pyx_v_parameters.strides[1]) )))) * (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_parameters.data + __pyx_t_85 * __pyx_v_parameters.strides[0]) ) + __pyx_t_86 * __pyx_v_parameters.strides[1]) ))));

+3686:                 term2 = term * x[i*M+m] / ntestpop

      __pyx_t_87 = ((__pyx_v_i * __pyx_v_M) + __pyx_v_m);
      __pyx_v_term2 = ((__pyx_v_term * (*((double *) ( /* dim=0 */ (__pyx_v_x.data + __pyx_t_87 * __pyx_v_x.strides[0]) )))) / __pyx_v_ntestpop);

+3687:                 J[i, m, i, m] -= term

      __pyx_t_88 = __pyx_v_i;
      __pyx_t_89 = __pyx_v_m;
      __pyx_t_90 = __pyx_v_i;
      __pyx_t_91 = __pyx_v_m;
      *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_88 * __pyx_v_J.strides[0]) ) + __pyx_t_89 * __pyx_v_J.strides[1]) ) + __pyx_t_90 * __pyx_v_J.strides[2]) ) + __pyx_t_91 * __pyx_v_J.strides[3]) )) -= __pyx_v_term;

+3688:                 J[i+nClassU, m, i, m] += term

      __pyx_t_92 = (__pyx_v_i + __pyx_v_nClassU);
      __pyx_t_93 = __pyx_v_m;
      __pyx_t_94 = __pyx_v_i;
      __pyx_t_95 = __pyx_v_m;
      *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_92 * __pyx_v_J.strides[0]) ) + __pyx_t_93 * __pyx_v_J.strides[1]) ) + __pyx_t_94 * __pyx_v_J.strides[2]) ) + __pyx_t_95 * __pyx_v_J.strides[3]) )) += __pyx_v_term;

+3689:                 J[nClass-1,  m, i, m] += term

      __pyx_t_96 = (__pyx_v_nClass - 1);
      __pyx_t_97 = __pyx_v_m;
      __pyx_t_98 = __pyx_v_i;
      __pyx_t_99 = __pyx_v_m;
      *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_96 * __pyx_v_J.strides[0]) ) + __pyx_t_97 * __pyx_v_J.strides[1]) ) + __pyx_t_98 * __pyx_v_J.strides[2]) ) + __pyx_t_99 * __pyx_v_J.strides[3]) )) += __pyx_v_term;

+3690:                 for n in range(M):

      __pyx_t_34 = __pyx_v_M;
      __pyx_t_35 = __pyx_t_34;
      for (__pyx_t_36 = 0; __pyx_t_36 < __pyx_t_35; __pyx_t_36+=1) {
        __pyx_v_n = __pyx_t_36;

+3691:                     for j in range(nClassUwoN):

        __pyx_t_100 = __pyx_v_nClassUwoN;
        __pyx_t_101 = __pyx_t_100;
        for (__pyx_t_102 = 0; __pyx_t_102 < __pyx_t_101; __pyx_t_102+=1) {
          __pyx_v_j = __pyx_t_102;

+3692:                         term3 = term2 * (parameters[test_freq[j],n] - parameters[test_freq[nClassUwoN],n])

          __pyx_t_103 = __pyx_v_j;
          __pyx_t_104 = (*((int *) ( /* dim=0 */ (__pyx_v_test_freq.data + __pyx_t_103 * __pyx_v_test_freq.strides[0]) )));
          __pyx_t_105 = __pyx_v_n;
          __pyx_t_106 = __pyx_v_nClassUwoN;
          __pyx_t_107 = (*((int *) ( /* dim=0 */ (__pyx_v_test_freq.data + __pyx_t_106 * __pyx_v_test_freq.strides[0]) )));
          __pyx_t_108 = __pyx_v_n;
          __pyx_v_term3 = (__pyx_v_term2 * ((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_parameters.data + __pyx_t_104 * __pyx_v_parameters.strides[0]) ) + __pyx_t_105 * __pyx_v_parameters.strides[1]) ))) - (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_parameters.data + __pyx_t_107 * __pyx_v_parameters.strides[0]) ) + __pyx_t_108 * __pyx_v_parameters.strides[1]) )))));

+3693:                         J[i, m, j, n] += term3

          __pyx_t_109 = __pyx_v_i;
          __pyx_t_110 = __pyx_v_m;
          __pyx_t_111 = __pyx_v_j;
          __pyx_t_112 = __pyx_v_n;
          *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_109 * __pyx_v_J.strides[0]) ) + __pyx_t_110 * __pyx_v_J.strides[1]) ) + __pyx_t_111 * __pyx_v_J.strides[2]) ) + __pyx_t_112 * __pyx_v_J.strides[3]) )) += __pyx_v_term3;

+3694:                         J[i+nClassU, m, j, n] -= term3

          __pyx_t_113 = (__pyx_v_i + __pyx_v_nClassU);
          __pyx_t_114 = __pyx_v_m;
          __pyx_t_115 = __pyx_v_j;
          __pyx_t_116 = __pyx_v_n;
          *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_113 * __pyx_v_J.strides[0]) ) + __pyx_t_114 * __pyx_v_J.strides[1]) ) + __pyx_t_115 * __pyx_v_J.strides[2]) ) + __pyx_t_116 * __pyx_v_J.strides[3]) )) -= __pyx_v_term3;

+3695:                         J[nClass-1,  m, j, n] -= term3

          __pyx_t_117 = (__pyx_v_nClass - 1);
          __pyx_t_118 = __pyx_v_m;
          __pyx_t_119 = __pyx_v_j;
          __pyx_t_120 = __pyx_v_n;
          *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_117 * __pyx_v_J.strides[0]) ) + __pyx_t_118 * __pyx_v_J.strides[1]) ) + __pyx_t_119 * __pyx_v_J.strides[2]) ) + __pyx_t_120 * __pyx_v_J.strides[3]) )) -= __pyx_v_term3;
        }

+3696:                     term3 = term2 * parameters[test_freq[nClassUwoN],n]

        __pyx_t_121 = __pyx_v_nClassUwoN;
        __pyx_t_122 = (*((int *) ( /* dim=0 */ (__pyx_v_test_freq.data + __pyx_t_121 * __pyx_v_test_freq.strides[0]) )));
        __pyx_t_123 = __pyx_v_n;
        __pyx_v_term3 = (__pyx_v_term2 * (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_parameters.data + __pyx_t_122 * __pyx_v_parameters.strides[0]) ) + __pyx_t_123 * __pyx_v_parameters.strides[1]) ))));

+3697:                     if self.constant_terms.size > 0:

        __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self->constant_terms), __pyx_n_s_size); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 3697, __pyx_L1_error)
        __Pyx_GOTREF(__pyx_t_2);
        __pyx_t_124 = PyObject_RichCompare(__pyx_t_2, __pyx_int_0, Py_GT); __Pyx_XGOTREF(__pyx_t_124); if (unlikely(!__pyx_t_124)) __PYX_ERR(0, 3697, __pyx_L1_error)
        __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
        __pyx_t_26 = __Pyx_PyObject_IsTrue(__pyx_t_124); if (unlikely(__pyx_t_26 < 0)) __PYX_ERR(0, 3697, __pyx_L1_error)
        __Pyx_DECREF(__pyx_t_124); __pyx_t_124 = 0;
        if (__pyx_t_26) {
/* … */
        }

+3698:                         J[i, m, nClassUwoN, n] += term3

          __pyx_t_125 = __pyx_v_i;
          __pyx_t_126 = __pyx_v_m;
          __pyx_t_127 = __pyx_v_nClassUwoN;
          __pyx_t_128 = __pyx_v_n;
          *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_125 * __pyx_v_J.strides[0]) ) + __pyx_t_126 * __pyx_v_J.strides[1]) ) + __pyx_t_127 * __pyx_v_J.strides[2]) ) + __pyx_t_128 * __pyx_v_J.strides[3]) )) += __pyx_v_term3;

+3699:                         J[i+nClassU, m, nClassUwoN, n] -= term3

          __pyx_t_129 = (__pyx_v_i + __pyx_v_nClassU);
          __pyx_t_130 = __pyx_v_m;
          __pyx_t_131 = __pyx_v_nClassUwoN;
          __pyx_t_132 = __pyx_v_n;
          *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_129 * __pyx_v_J.strides[0]) ) + __pyx_t_130 * __pyx_v_J.strides[1]) ) + __pyx_t_131 * __pyx_v_J.strides[2]) ) + __pyx_t_132 * __pyx_v_J.strides[3]) )) -= __pyx_v_term3;

+3700:                         J[nClass-1,  m, nClassUwoN, n] -= term3

          __pyx_t_133 = (__pyx_v_nClass - 1);
          __pyx_t_134 = __pyx_v_m;
          __pyx_t_135 = __pyx_v_nClassUwoN;
          __pyx_t_136 = __pyx_v_n;
          *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_133 * __pyx_v_J.strides[0]) ) + __pyx_t_134 * __pyx_v_J.strides[1]) ) + __pyx_t_135 * __pyx_v_J.strides[2]) ) + __pyx_t_136 * __pyx_v_J.strides[3]) )) -= __pyx_v_term3;

+3701:                     J[i, m, nClass-1, n] -= term3

        __pyx_t_137 = __pyx_v_i;
        __pyx_t_138 = __pyx_v_m;
        __pyx_t_139 = (__pyx_v_nClass - 1);
        __pyx_t_140 = __pyx_v_n;
        *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_137 * __pyx_v_J.strides[0]) ) + __pyx_t_138 * __pyx_v_J.strides[1]) ) + __pyx_t_139 * __pyx_v_J.strides[2]) ) + __pyx_t_140 * __pyx_v_J.strides[3]) )) -= __pyx_v_term3;

+3702:                     J[i+nClassU, m, nClass-1, n] += term3

        __pyx_t_141 = (__pyx_v_i + __pyx_v_nClassU);
        __pyx_t_142 = __pyx_v_m;
        __pyx_t_143 = (__pyx_v_nClass - 1);
        __pyx_t_144 = __pyx_v_n;
        *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_141 * __pyx_v_J.strides[0]) ) + __pyx_t_142 * __pyx_v_J.strides[1]) ) + __pyx_t_143 * __pyx_v_J.strides[2]) ) + __pyx_t_144 * __pyx_v_J.strides[3]) )) += __pyx_v_term3;

+3703:                     J[nClass-1,  m, nClass-1, n] += term3

        __pyx_t_145 = (__pyx_v_nClass - 1);
        __pyx_t_146 = __pyx_v_m;
        __pyx_t_147 = (__pyx_v_nClass - 1);
        __pyx_t_148 = __pyx_v_n;
        *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_145 * __pyx_v_J.strides[0]) ) + __pyx_t_146 * __pyx_v_J.strides[1]) ) + __pyx_t_147 * __pyx_v_J.strides[2]) ) + __pyx_t_148 * __pyx_v_J.strides[3]) )) += __pyx_v_term3;
      }
    }

+3704:             term = tau0 * parameters[test_freq[nClassUwoN], m] * parameters[test_pos[nClassUwoN], m]

    __pyx_t_149 = __pyx_v_nClassUwoN;
    __pyx_t_150 = (*((int *) ( /* dim=0 */ (__pyx_v_test_freq.data + __pyx_t_149 * __pyx_v_test_freq.strides[0]) )));
    __pyx_t_151 = __pyx_v_m;
    __pyx_t_152 = __pyx_v_nClassUwoN;
    __pyx_t_153 = (*((int *) ( /* dim=0 */ (__pyx_v_test_pos.data + __pyx_t_152 * __pyx_v_test_pos.strides[0]) )));
    __pyx_t_154 = __pyx_v_m;
    __pyx_v_term = ((__pyx_v_tau0 * (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_parameters.data + __pyx_t_150 * __pyx_v_parameters.strides[0]) ) + __pyx_t_151 * __pyx_v_parameters.strides[1]) )))) * (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_parameters.data + __pyx_t_153 * __pyx_v_parameters.strides[0]) ) + __pyx_t_154 * __pyx_v_parameters.strides[1]) ))));

+3705:             term2 = term * r[m] / ntestpop

    __pyx_t_155 = __pyx_v_m;
    __pyx_v_term2 = ((__pyx_v_term * (*((double *) ( /* dim=0 */ (__pyx_v_r.data + __pyx_t_155 * __pyx_v_r.strides[0]) )))) / __pyx_v_ntestpop);

+3706:             for j in range(nClassUwoN):

    __pyx_t_16 = __pyx_v_nClassUwoN;
    __pyx_t_17 = __pyx_t_16;
    for (__pyx_t_18 = 0; __pyx_t_18 < __pyx_t_17; __pyx_t_18+=1) {
      __pyx_v_j = __pyx_t_18;

+3707:                 J[nClass-1, m, j, m] -= term

      __pyx_t_156 = (__pyx_v_nClass - 1);
      __pyx_t_157 = __pyx_v_m;
      __pyx_t_158 = __pyx_v_j;
      __pyx_t_159 = __pyx_v_m;
      *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_156 * __pyx_v_J.strides[0]) ) + __pyx_t_157 * __pyx_v_J.strides[1]) ) + __pyx_t_158 * __pyx_v_J.strides[2]) ) + __pyx_t_159 * __pyx_v_J.strides[3]) )) -= __pyx_v_term;
    }

+3708:             if self.constant_terms.size > 0:

    __pyx_t_124 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self->constant_terms), __pyx_n_s_size); if (unlikely(!__pyx_t_124)) __PYX_ERR(0, 3708, __pyx_L1_error)
    __Pyx_GOTREF(__pyx_t_124);
    __pyx_t_2 = PyObject_RichCompare(__pyx_t_124, __pyx_int_0, Py_GT); __Pyx_XGOTREF(__pyx_t_2); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 3708, __pyx_L1_error)
    __Pyx_DECREF(__pyx_t_124); __pyx_t_124 = 0;
    __pyx_t_26 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_26 < 0)) __PYX_ERR(0, 3708, __pyx_L1_error)
    __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
    if (__pyx_t_26) {
/* … */
    }

+3709:                 J[nClass-1, m, nClassUwoN, m] += term

      __pyx_t_160 = (__pyx_v_nClass - 1);
      __pyx_t_161 = __pyx_v_m;
      __pyx_t_162 = __pyx_v_nClassUwoN;
      __pyx_t_163 = __pyx_v_m;
      *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_160 * __pyx_v_J.strides[0]) ) + __pyx_t_161 * __pyx_v_J.strides[1]) ) + __pyx_t_162 * __pyx_v_J.strides[2]) ) + __pyx_t_163 * __pyx_v_J.strides[3]) )) += __pyx_v_term;

+3710:             J[nClass-1, m, nClass-1, m] -= term

    __pyx_t_164 = (__pyx_v_nClass - 1);
    __pyx_t_165 = __pyx_v_m;
    __pyx_t_166 = (__pyx_v_nClass - 1);
    __pyx_t_167 = __pyx_v_m;
    *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_J.data + __pyx_t_164 * __pyx_v_J.strides[0]) ) + __pyx_t_165 * __pyx_v_J.strides[1]) ) + __pyx_t_166 * __pyx_v_J.strides[2]) ) + __pyx_t_167 * __pyx_v_J.strides[3]) )) -= __pyx_v_term;

+3711:             for n in range(M):

    __pyx_t_16 = __pyx_v_M;
    __pyx_t_17 = __pyx_t_16;
    for (__pyx_t_18 = 0; __pyx_t_18 < __pyx_t_17; __pyx_t_18+=1) {
      __pyx_v_n = __pyx_t_18;

+3712:                 for j in range(nClassUwoN):

      __pyx_t_34 = __pyx_v_nClassUwoN;
      __pyx_t_35 = __pyx_t_34;
      for (__pyx_t_36 = 0; __pyx_t_36 < __pyx_t_35; __pyx_t_36+=1) {
        __pyx_v_j = __pyx_t_36;

+3713:                     term3 = term2 * (parameters[test_freq[j],n] - parameters[test_freq[nClassUwoN],n])

        __pyx_t_168 = __pyx_v_j;
        __pyx_t_169 = (*((int *) ( /* dim=0 */ (__pyx_v_test_freq.data + __pyx_t_168 * __pyx_v_test_freq.strides[0]) )));
        __pyx_t_170 = __pyx_v_n;
        __pyx_t_171 = __pyx_v_nClassUwoN;
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        *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_B.data + __pyx_t_27 * __pyx_v_B.strides[0]) ) + __pyx_t_28 * __pyx_v_B.strides[1]) ) + __pyx_t_29 * __pyx_v_B.strides[2]) ) + __pyx_t_30 * __pyx_v_B.strides[3]) )) += ((*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_26 * __pyx_v_rate.strides[0]) ))) / __pyx_v_N);
      }
    }

 3749: 

+3750:         for i in range(infection_terms.shape[0]):

  __pyx_t_1 = (__pyx_v_infection_terms.shape[0]);
  __pyx_t_12 = __pyx_t_1;
  for (__pyx_t_13 = 0; __pyx_t_13 < __pyx_t_12; __pyx_t_13+=1) {
    __pyx_v_i = __pyx_t_13;

+3751:             product_index = infection_terms[i, 2]

    __pyx_t_31 = __pyx_v_i;
    __pyx_t_32 = 2;
    __pyx_v_product_index = (*((int *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_infection_terms.data + __pyx_t_31 * __pyx_v_infection_terms.strides[0]) ) + __pyx_t_32 * __pyx_v_infection_terms.strides[1]) )));

+3752:             infective_index = infection_terms[i, 1]

    __pyx_t_33 = __pyx_v_i;
    __pyx_t_34 = 1;
    __pyx_v_infective_index = (*((int *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_infection_terms.data + __pyx_t_33 * __pyx_v_infection_terms.strides[0]) ) + __pyx_t_34 * __pyx_v_infection_terms.strides[1]) )));

+3753:             rate_index = infection_terms[i, 0]

    __pyx_t_35 = __pyx_v_i;
    __pyx_t_36 = 0;
    __pyx_v_rate_index = (*((int *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_infection_terms.data + __pyx_t_35 * __pyx_v_infection_terms.strides[0]) ) + __pyx_t_36 * __pyx_v_infection_terms.strides[1]) )));

+3754:             rate = parameters[rate_index]

    __pyx_t_8.data = __pyx_v_parameters.data;
    __pyx_t_8.memview = __pyx_v_parameters.memview;
    __PYX_INC_MEMVIEW(&__pyx_t_8, 0);
    {
    Py_ssize_t __pyx_tmp_idx = __pyx_v_rate_index;
    Py_ssize_t __pyx_tmp_stride = __pyx_v_parameters.strides[0];
        if ((0)) __PYX_ERR(0, 3754, __pyx_L1_error)
        __pyx_t_8.data += __pyx_tmp_idx * __pyx_tmp_stride;
}

__pyx_t_8.shape[0] = __pyx_v_parameters.shape[1];
__pyx_t_8.strides[0] = __pyx_v_parameters.strides[1];
    __pyx_t_8.suboffsets[0] = -1;

__PYX_XDEC_MEMVIEW(&__pyx_v_rate, 1);
    __pyx_v_rate = __pyx_t_8;
    __pyx_t_8.memview = NULL;
    __pyx_t_8.data = NULL;

+3755:             for m in range(M):

    __pyx_t_18 = __pyx_v_M;
    __pyx_t_19 = __pyx_t_18;
    for (__pyx_t_20 = 0; __pyx_t_20 < __pyx_t_19; __pyx_t_20+=1) {
      __pyx_v_m = __pyx_t_20;

+3756:                 B[S_index, m, S_index, m] += rate[m]*l[i, m]*s[m]

      __pyx_t_37 = __pyx_v_m;
      __pyx_t_38 = __pyx_v_i;
      __pyx_t_39 = __pyx_v_m;
      __pyx_t_40 = __pyx_v_m;
      __pyx_t_41 = __pyx_v_S_index;
      __pyx_t_42 = __pyx_v_m;
      __pyx_t_43 = __pyx_v_S_index;
      __pyx_t_44 = __pyx_v_m;
      *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_B.data + __pyx_t_41 * __pyx_v_B.strides[0]) ) + __pyx_t_42 * __pyx_v_B.strides[1]) ) + __pyx_t_43 * __pyx_v_B.strides[2]) ) + __pyx_t_44 * __pyx_v_B.strides[3]) )) += (((*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_37 * __pyx_v_rate.strides[0]) ))) * (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_l.data + __pyx_t_38 * __pyx_v_l.strides[0]) ) + __pyx_t_39 * __pyx_v_l.strides[1]) )))) * (*((double *) ( /* dim=0 */ (__pyx_v_s.data + __pyx_t_40 * __pyx_v_s.strides[0]) ))));

+3757:                 if product_index>-1:

      __pyx_t_11 = ((__pyx_v_product_index > -1L) != 0);
      if (__pyx_t_11) {
/* … */
      }
    }
  }

+3758:                     B[S_index, m, product_index, m] -=  rate[m]*l[i, m]*s[m]

        __pyx_t_45 = __pyx_v_m;
        __pyx_t_46 = __pyx_v_i;
        __pyx_t_47 = __pyx_v_m;
        __pyx_t_48 = __pyx_v_m;
        __pyx_t_49 = __pyx_v_S_index;
        __pyx_t_50 = __pyx_v_m;
        __pyx_t_51 = __pyx_v_product_index;
        __pyx_t_52 = __pyx_v_m;
        *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_B.data + __pyx_t_49 * __pyx_v_B.strides[0]) ) + __pyx_t_50 * __pyx_v_B.strides[1]) ) + __pyx_t_51 * __pyx_v_B.strides[2]) ) + __pyx_t_52 * __pyx_v_B.strides[3]) )) -= (((*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_45 * __pyx_v_rate.strides[0]) ))) * (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_l.data + __pyx_t_46 * __pyx_v_l.strides[0]) ) + __pyx_t_47 * __pyx_v_l.strides[1]) )))) * (*((double *) ( /* dim=0 */ (__pyx_v_s.data + __pyx_t_48 * __pyx_v_s.strides[0]) ))));

+3759:                     B[product_index, m, product_index, m] += rate[m]*l[i, m]*s[m]

        __pyx_t_53 = __pyx_v_m;
        __pyx_t_54 = __pyx_v_i;
        __pyx_t_55 = __pyx_v_m;
        __pyx_t_56 = __pyx_v_m;
        __pyx_t_57 = __pyx_v_product_index;
        __pyx_t_58 = __pyx_v_m;
        __pyx_t_59 = __pyx_v_product_index;
        __pyx_t_60 = __pyx_v_m;
        *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_B.data + __pyx_t_57 * __pyx_v_B.strides[0]) ) + __pyx_t_58 * __pyx_v_B.strides[1]) ) + __pyx_t_59 * __pyx_v_B.strides[2]) ) + __pyx_t_60 * __pyx_v_B.strides[3]) )) += (((*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_53 * __pyx_v_rate.strides[0]) ))) * (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_l.data + __pyx_t_54 * __pyx_v_l.strides[0]) ) + __pyx_t_55 * __pyx_v_l.strides[1]) )))) * (*((double *) ( /* dim=0 */ (__pyx_v_s.data + __pyx_t_56 * __pyx_v_s.strides[0]) ))));

+3760:                     B[product_index, m, S_index, m] -= rate[m]*l[i, m]*s[m]

        __pyx_t_61 = __pyx_v_m;
        __pyx_t_62 = __pyx_v_i;
        __pyx_t_63 = __pyx_v_m;
        __pyx_t_64 = __pyx_v_m;
        __pyx_t_65 = __pyx_v_product_index;
        __pyx_t_66 = __pyx_v_m;
        __pyx_t_67 = __pyx_v_S_index;
        __pyx_t_68 = __pyx_v_m;
        *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_B.data + __pyx_t_65 * __pyx_v_B.strides[0]) ) + __pyx_t_66 * __pyx_v_B.strides[1]) ) + __pyx_t_67 * __pyx_v_B.strides[2]) ) + __pyx_t_68 * __pyx_v_B.strides[3]) )) -= (((*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_61 * __pyx_v_rate.strides[0]) ))) * (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_l.data + __pyx_t_62 * __pyx_v_l.strides[0]) ) + __pyx_t_63 * __pyx_v_l.strides[1]) )))) * (*((double *) ( /* dim=0 */ (__pyx_v_s.data + __pyx_t_64 * __pyx_v_s.strides[0]) ))));

 3761: 

+3762:         for i in range(linear_terms.shape[0]):

  __pyx_t_1 = (__pyx_v_linear_terms.shape[0]);
  __pyx_t_12 = __pyx_t_1;
  for (__pyx_t_13 = 0; __pyx_t_13 < __pyx_t_12; __pyx_t_13+=1) {
    __pyx_v_i = __pyx_t_13;

+3763:             product_index = linear_terms[i, 2]

    __pyx_t_69 = __pyx_v_i;
    __pyx_t_70 = 2;
    __pyx_v_product_index = (*((int *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_linear_terms.data + __pyx_t_69 * __pyx_v_linear_terms.strides[0]) ) + __pyx_t_70 * __pyx_v_linear_terms.strides[1]) )));

+3764:             reagent_index = linear_terms[i, 1]

    __pyx_t_71 = __pyx_v_i;
    __pyx_t_72 = 1;
    __pyx_v_reagent_index = (*((int *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_linear_terms.data + __pyx_t_71 * __pyx_v_linear_terms.strides[0]) ) + __pyx_t_72 * __pyx_v_linear_terms.strides[1]) )));

+3765:             reagent = x[reagent_index*M:(reagent_index+1)*M]

    __pyx_t_8.data = __pyx_v_x.data;
    __pyx_t_8.memview = __pyx_v_x.memview;
    __PYX_INC_MEMVIEW(&__pyx_t_8, 0);
    __pyx_t_9 = -1;
    if (unlikely(__pyx_memoryview_slice_memviewslice(
    &__pyx_t_8,
    __pyx_v_x.shape[0], __pyx_v_x.strides[0], __pyx_v_x.suboffsets[0],
    0,
    0,
    &__pyx_t_9,
    (__pyx_v_reagent_index * __pyx_v_M),
    ((__pyx_v_reagent_index + 1) * __pyx_v_M),
    0,
    1,
    1,
    0,
    1) < 0))
{
    __PYX_ERR(0, 3765, __pyx_L1_error)
}

__PYX_XDEC_MEMVIEW(&__pyx_v_reagent, 1);
    __pyx_v_reagent = __pyx_t_8;
    __pyx_t_8.memview = NULL;
    __pyx_t_8.data = NULL;

+3766:             rate_index = linear_terms[i, 0]

    __pyx_t_73 = __pyx_v_i;
    __pyx_t_74 = 0;
    __pyx_v_rate_index = (*((int *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_linear_terms.data + __pyx_t_73 * __pyx_v_linear_terms.strides[0]) ) + __pyx_t_74 * __pyx_v_linear_terms.strides[1]) )));

+3767:             rate = parameters[rate_index]

    __pyx_t_8.data = __pyx_v_parameters.data;
    __pyx_t_8.memview = __pyx_v_parameters.memview;
    __PYX_INC_MEMVIEW(&__pyx_t_8, 0);
    {
    Py_ssize_t __pyx_tmp_idx = __pyx_v_rate_index;
    Py_ssize_t __pyx_tmp_stride = __pyx_v_parameters.strides[0];
        if ((0)) __PYX_ERR(0, 3767, __pyx_L1_error)
        __pyx_t_8.data += __pyx_tmp_idx * __pyx_tmp_stride;
}

__pyx_t_8.shape[0] = __pyx_v_parameters.shape[1];
__pyx_t_8.strides[0] = __pyx_v_parameters.strides[1];
    __pyx_t_8.suboffsets[0] = -1;

__PYX_XDEC_MEMVIEW(&__pyx_v_rate, 1);
    __pyx_v_rate = __pyx_t_8;
    __pyx_t_8.memview = NULL;
    __pyx_t_8.data = NULL;

+3768:             for m in range(M): # only fill in the upper triangular form

    __pyx_t_18 = __pyx_v_M;
    __pyx_t_19 = __pyx_t_18;
    for (__pyx_t_20 = 0; __pyx_t_20 < __pyx_t_19; __pyx_t_20+=1) {
      __pyx_v_m = __pyx_t_20;

+3769:                 B[reagent_index, m, reagent_index, m] += rate[m]*reagent[m]

      __pyx_t_75 = __pyx_v_m;
      __pyx_t_76 = __pyx_v_m;
      __pyx_t_77 = __pyx_v_reagent_index;
      __pyx_t_78 = __pyx_v_m;
      __pyx_t_79 = __pyx_v_reagent_index;
      __pyx_t_80 = __pyx_v_m;
      *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_B.data + __pyx_t_77 * __pyx_v_B.strides[0]) ) + __pyx_t_78 * __pyx_v_B.strides[1]) ) + __pyx_t_79 * __pyx_v_B.strides[2]) ) + __pyx_t_80 * __pyx_v_B.strides[3]) )) += ((*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_75 * __pyx_v_rate.strides[0]) ))) * (*((double *) ( /* dim=0 */ (__pyx_v_reagent.data + __pyx_t_76 * __pyx_v_reagent.strides[0]) ))));

+3770:                 if product_index>-1:

      __pyx_t_11 = ((__pyx_v_product_index > -1L) != 0);
      if (__pyx_t_11) {
/* … */
      }
    }

+3771:                     B[product_index, m, product_index, m] += rate[m]*reagent[m]

        __pyx_t_81 = __pyx_v_m;
        __pyx_t_82 = __pyx_v_m;
        __pyx_t_83 = __pyx_v_product_index;
        __pyx_t_84 = __pyx_v_m;
        __pyx_t_85 = __pyx_v_product_index;
        __pyx_t_86 = __pyx_v_m;
        *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_B.data + __pyx_t_83 * __pyx_v_B.strides[0]) ) + __pyx_t_84 * __pyx_v_B.strides[1]) ) + __pyx_t_85 * __pyx_v_B.strides[2]) ) + __pyx_t_86 * __pyx_v_B.strides[3]) )) += ((*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_81 * __pyx_v_rate.strides[0]) ))) * (*((double *) ( /* dim=0 */ (__pyx_v_reagent.data + __pyx_t_82 * __pyx_v_reagent.strides[0]) ))));

+3772:                     B[reagent_index, m, product_index, m] += -rate[m]*reagent[m]

        __pyx_t_87 = __pyx_v_m;
        __pyx_t_88 = __pyx_v_m;
        __pyx_t_89 = __pyx_v_reagent_index;
        __pyx_t_90 = __pyx_v_m;
        __pyx_t_91 = __pyx_v_product_index;
        __pyx_t_92 = __pyx_v_m;
        *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_B.data + __pyx_t_89 * __pyx_v_B.strides[0]) ) + __pyx_t_90 * __pyx_v_B.strides[1]) ) + __pyx_t_91 * __pyx_v_B.strides[2]) ) + __pyx_t_92 * __pyx_v_B.strides[3]) )) += ((-(*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_87 * __pyx_v_rate.strides[0]) )))) * (*((double *) ( /* dim=0 */ (__pyx_v_reagent.data + __pyx_t_88 * __pyx_v_reagent.strides[0]) ))));

+3773:                     B[product_index, m, reagent_index, m] += -rate[m]*reagent[m]

        __pyx_t_93 = __pyx_v_m;
        __pyx_t_94 = __pyx_v_m;
        __pyx_t_95 = __pyx_v_product_index;
        __pyx_t_96 = __pyx_v_m;
        __pyx_t_97 = __pyx_v_reagent_index;
        __pyx_t_98 = __pyx_v_m;
        *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_B.data + __pyx_t_95 * __pyx_v_B.strides[0]) ) + __pyx_t_96 * __pyx_v_B.strides[1]) ) + __pyx_t_97 * __pyx_v_B.strides[2]) ) + __pyx_t_98 * __pyx_v_B.strides[3]) )) += ((-(*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_93 * __pyx_v_rate.strides[0]) )))) * (*((double *) ( /* dim=0 */ (__pyx_v_reagent.data + __pyx_t_94 * __pyx_v_reagent.strides[0]) ))));

 3774:             # same transitions in Q classes

+3775:             reagent = x[(reagent_index+nClassU)*M:((reagent_index+nClassU)+1)*M]

    __pyx_t_8.data = __pyx_v_x.data;
    __pyx_t_8.memview = __pyx_v_x.memview;
    __PYX_INC_MEMVIEW(&__pyx_t_8, 0);
    __pyx_t_9 = -1;
    if (unlikely(__pyx_memoryview_slice_memviewslice(
    &__pyx_t_8,
    __pyx_v_x.shape[0], __pyx_v_x.strides[0], __pyx_v_x.suboffsets[0],
    0,
    0,
    &__pyx_t_9,
    ((__pyx_v_reagent_index + __pyx_v_nClassU) * __pyx_v_M),
    (((__pyx_v_reagent_index + __pyx_v_nClassU) + 1) * __pyx_v_M),
    0,
    1,
    1,
    0,
    1) < 0))
{
    __PYX_ERR(0, 3775, __pyx_L1_error)
}

__PYX_XDEC_MEMVIEW(&__pyx_v_reagent, 1);
    __pyx_v_reagent = __pyx_t_8;
    __pyx_t_8.memview = NULL;
    __pyx_t_8.data = NULL;

+3776:             for m in range(M): # only fill in the upper triangular form

    __pyx_t_18 = __pyx_v_M;
    __pyx_t_19 = __pyx_t_18;
    for (__pyx_t_20 = 0; __pyx_t_20 < __pyx_t_19; __pyx_t_20+=1) {
      __pyx_v_m = __pyx_t_20;

+3777:                 B[reagent_index+nClassU, m, reagent_index+nClassU, m] += rate[m]*reagent[m]

      __pyx_t_99 = __pyx_v_m;
      __pyx_t_100 = __pyx_v_m;
      __pyx_t_101 = (__pyx_v_reagent_index + __pyx_v_nClassU);
      __pyx_t_102 = __pyx_v_m;
      __pyx_t_103 = (__pyx_v_reagent_index + __pyx_v_nClassU);
      __pyx_t_104 = __pyx_v_m;
      *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_B.data + __pyx_t_101 * __pyx_v_B.strides[0]) ) + __pyx_t_102 * __pyx_v_B.strides[1]) ) + __pyx_t_103 * __pyx_v_B.strides[2]) ) + __pyx_t_104 * __pyx_v_B.strides[3]) )) += ((*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_99 * __pyx_v_rate.strides[0]) ))) * (*((double *) ( /* dim=0 */ (__pyx_v_reagent.data + __pyx_t_100 * __pyx_v_reagent.strides[0]) ))));

+3778:                 if product_index>-1:

      __pyx_t_11 = ((__pyx_v_product_index > -1L) != 0);
      if (__pyx_t_11) {
/* … */
      }
    }
  }

+3779:                     B[product_index+nClassU, m, product_index+nClassU, m] += rate[m]*reagent[m]

        __pyx_t_105 = __pyx_v_m;
        __pyx_t_106 = __pyx_v_m;
        __pyx_t_107 = (__pyx_v_product_index + __pyx_v_nClassU);
        __pyx_t_108 = __pyx_v_m;
        __pyx_t_109 = (__pyx_v_product_index + __pyx_v_nClassU);
        __pyx_t_110 = __pyx_v_m;
        *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_B.data + __pyx_t_107 * __pyx_v_B.strides[0]) ) + __pyx_t_108 * __pyx_v_B.strides[1]) ) + __pyx_t_109 * __pyx_v_B.strides[2]) ) + __pyx_t_110 * __pyx_v_B.strides[3]) )) += ((*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_105 * __pyx_v_rate.strides[0]) ))) * (*((double *) ( /* dim=0 */ (__pyx_v_reagent.data + __pyx_t_106 * __pyx_v_reagent.strides[0]) ))));

+3780:                     B[reagent_index+nClassU, m, product_index+nClassU, m] += -rate[m]*reagent[m]

        __pyx_t_111 = __pyx_v_m;
        __pyx_t_112 = __pyx_v_m;
        __pyx_t_113 = (__pyx_v_reagent_index + __pyx_v_nClassU);
        __pyx_t_114 = __pyx_v_m;
        __pyx_t_115 = (__pyx_v_product_index + __pyx_v_nClassU);
        __pyx_t_116 = __pyx_v_m;
        *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_B.data + __pyx_t_113 * __pyx_v_B.strides[0]) ) + __pyx_t_114 * __pyx_v_B.strides[1]) ) + __pyx_t_115 * __pyx_v_B.strides[2]) ) + __pyx_t_116 * __pyx_v_B.strides[3]) )) += ((-(*((double *) ( /* dim=0 */ (__pyx_v_rate.data + __pyx_t_111 * __pyx_v_rate.strides[0]) )))) * (*((double *) ( /* dim=0 */ (__pyx_v_reagent.data + __pyx_t_112 * __pyx_v_reagent.strides[0]) ))));

+3781:                     B[product_index+nClassU, m, reagent_index+nClassU, m] += -rate[m]*reagent[m]

        __pyx_t_117 = __pyx_v_m;
        __pyx_t_118 = __pyx_v_m;
        __pyx_t_119 = (__pyx_v_product_index + __pyx_v_nClassU);
        __pyx_t_120 = __pyx_v_m;
        __pyx_t_121 = (__pyx_v_reagent_index + __pyx_v_nClassU);
        __pyx_t_122 = __pyx_v_m;
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 3782: 

+3783:         for m in range(M):

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+3784:             for i in range(nClassUwoN): # only fill in the upper triangular form

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+3785:                 term = tau0 * parameters[test_freq[i], m] * parameters[test_pos[i], m] * x[m+M*i]

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      __pyx_t_124 = (*((int *) ( /* dim=0 */ (__pyx_v_test_freq.data + __pyx_t_123 * __pyx_v_test_freq.strides[0]) )));
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+3786:                 B[i, m, i, m] += term

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      __pyx_t_132 = __pyx_v_i;
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+3787:                 B[i+nClassU, m, i+nClassU, m] += term

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+3788:                 B[nClass-1, m, nClass-1, m] += term

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+3789:                 B[i, m, i+nClassU, m] -= term

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+3790:                 B[i+nClassU, m, i, m] -= term

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+3791:                 B[i, m, nClass-1, m] -= term

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      __pyx_t_151 = __pyx_v_m;
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+3792:                 B[nClass-1, m, i, m] -= term

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+3793:                 B[i+nClassU, m, nClass-1, m] += term

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      __pyx_t_159 = __pyx_v_m;
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+3794:                 B[nClass-1, m, i+nClassU, m] += term

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      __pyx_t_165 = __pyx_v_m;
      *((double *) ( /* dim=3 */ (( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_B.data + __pyx_t_162 * __pyx_v_B.strides[0]) ) + __pyx_t_163 * __pyx_v_B.strides[1]) ) + __pyx_t_164 * __pyx_v_B.strides[2]) ) + __pyx_t_165 * __pyx_v_B.strides[3]) )) += __pyx_v_term;
    }

+3795:             term = tau0 * parameters[test_freq[nClassUwoN], m] * parameters[test_pos[nClassUwoN], m] * r[m]

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+3796:             B[nClass-1, m, nClass-1, m] += term

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 3797: 

 3798: 

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